{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceType":"datasetVersion","sourceId":8760277,"datasetId":4299235,"databundleVersionId":8915569},{"sourceType":"datasetVersion","sourceId":8757146,"datasetId":5134215,"databundleVersionId":8912293},{"sourceType":"datasetVersion","sourceId":5960502,"datasetId":3418322,"databundleVersionId":6038183}],"dockerImageVersionId":30673,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install lightgbm","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:54:21.045466Z","iopub.execute_input":"2024-06-23T10:54:21.046168Z","iopub.status.idle":"2024-06-23T10:54:33.966867Z","shell.execute_reply.started":"2024-06-23T10:54:21.046137Z","shell.execute_reply":"2024-06-23T10:54:33.965770Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Requirement already satisfied: lightgbm in /opt/conda/lib/python3.10/site-packages (4.2.0)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from lightgbm) (1.26.4)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (from lightgbm) (1.11.4)\n","output_type":"stream"}]},{"cell_type":"code","source":"#!pip install --upgrade scikit-learn==1.4.0\n#!pip show scikit-learn","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:54:33.968610Z","iopub.execute_input":"2024-06-23T10:54:33.968915Z","iopub.status.idle":"2024-06-23T10:54:33.975197Z","shell.execute_reply.started":"2024-06-23T10:54:33.968887Z","shell.execute_reply":"2024-06-23T10:54:33.974097Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"from imblearn.over_sampling import BorderlineSMOTE\nfrom imblearn.over_sampling import KMeansSMOTE","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:54:33.976289Z","iopub.execute_input":"2024-06-23T10:54:33.977235Z","iopub.status.idle":"2024-06-23T10:54:35.421669Z","shell.execute_reply.started":"2024-06-23T10:54:33.977201Z","shell.execute_reply":"2024-06-23T10:54:35.420725Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"!pip show joblib","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:54:35.424154Z","iopub.execute_input":"2024-06-23T10:54:35.424634Z","iopub.status.idle":"2024-06-23T10:54:47.083377Z","shell.execute_reply.started":"2024-06-23T10:54:35.424600Z","shell.execute_reply":"2024-06-23T10:54:47.082385Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Name: joblib\nVersion: 1.3.2\nSummary: Lightweight pipelining with Python functions\nHome-page: \nAuthor: \nAuthor-email: Gael Varoquaux <gael.varoquaux@normalesup.org>\nLicense: BSD 3-Clause\nLocation: /opt/conda/lib/python3.10/site-packages\nRequires: \nRequired-by: cesium, cleverhans, contextily, cuml, fitter, gplearn, imbalanced-learn, kmodes, librosa, mlxtend, nilearn, phik, pyLDAvis, pynndescent, raft-dask, rgf-python, scikit-learn, scikit-optimize, scikit-plot, scikit-surprise, segregation, tobler, TPOT\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport os\n#from collections import Counter\n#from catboost import CatBoostClassifier\nfrom sklearn.model_selection import train_test_split\n#from imblearn.over_sampling import SMOTE\nfrom sklearn import metrics\n#from sklearn.feature_selection import VarianceThreshold\nfrom sklearn.datasets import make_classification\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import cross_val_score\n# xgboost для классификации\n#from xgboost import XGBClassifier\nfrom sklearn.metrics import f1_score\nfrom joblib import dump\nfrom sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.ensemble import ExtraTreesClassifier\nfrom sklearn.model_selection import cross_val_score","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:54:47.084793Z","iopub.execute_input":"2024-06-23T10:54:47.085117Z","iopub.status.idle":"2024-06-23T10:54:50.818192Z","shell.execute_reply.started":"2024-06-23T10:54:47.085083Z","shell.execute_reply":"2024-06-23T10:54:50.817357Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/dask/dataframe/_pyarrow_compat.py:23: UserWarning: You are using pyarrow version 11.0.0 which is known to be insecure. See https://www.cve.org/CVERecord?id=CVE-2023-47248 for further details. Please upgrade to pyarrow>=14.0.1 or install pyarrow-hotfix to patch your current version.\n  warnings.warn(\n","output_type":"stream"}]},{"cell_type":"code","source":"path = '/kaggle/input/ptb-xl-a-large-publicly-available2/ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.3/'\n\nscp_data = pd.read_csv(path + 'scp_statements.csv')\n","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:54:50.819335Z","iopub.execute_input":"2024-06-23T10:54:50.819632Z","iopub.status.idle":"2024-06-23T10:54:50.842253Z","shell.execute_reply.started":"2024-06-23T10:54:50.819605Z","shell.execute_reply":"2024-06-23T10:54:50.841462Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"path = '/kaggle/input/electrocardiogram-database-for-arrhythmia-study/ecg_df_diagnostic.csv'\n\necg_df_HTV = pd.read_csv(path, index_col=0 )\n","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:54:50.843514Z","iopub.execute_input":"2024-06-23T10:54:50.844199Z","iopub.status.idle":"2024-06-23T10:54:58.487034Z","shell.execute_reply.started":"2024-06-23T10:54:50.844162Z","shell.execute_reply":"2024-06-23T10:54:58.486218Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"len(ecg_df_HTV.columns[:-6])/12","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:54:58.488204Z","iopub.execute_input":"2024-06-23T10:54:58.488491Z","iopub.status.idle":"2024-06-23T10:54:58.495470Z","shell.execute_reply.started":"2024-06-23T10:54:58.488466Z","shell.execute_reply":"2024-06-23T10:54:58.494593Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"37.75"},"metadata":{}}]},{"cell_type":"code","source":"def sort_by_diagnostic(name_column):\n    list_diagnostic = []\n    for i in ecg_df_HTV[\"diagnostic\"].index:\n        k = ecg_df_HTV[\"diagnostic\"][i].split(\",\")\n        if len(k) > 1:\n            k.sort()\n            for i in range(0,len(k)):\n                for j in range(0,len(scp_data[scp_data.diagnostic ==1 ])):\n                    if (scp_data[scp_data.diagnostic ==1 ][\"Unnamed: 0\"][j]) == k[i] : \n                        k[i] = scp_data[scp_data.diagnostic ==1 ][name_column][j]\n            k.sort()\n            k = list(dict.fromkeys(k))\n        k = \",\".join(k)\n        list_diagnostic.append(k)\n    return  list_diagnostic\n#ecg_df_HTV[\"diagnostic_2\"] = sort_by_diagnostic(\"diagnostic_subclass\")","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:54:58.496714Z","iopub.execute_input":"2024-06-23T10:54:58.497339Z","iopub.status.idle":"2024-06-23T10:54:58.506899Z","shell.execute_reply.started":"2024-06-23T10:54:58.497312Z","shell.execute_reply":"2024-06-23T10:54:58.506112Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV[\"diagnostic\"].value_counts()[:20]","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:54:58.511122Z","iopub.execute_input":"2024-06-23T10:54:58.511397Z","iopub.status.idle":"2024-06-23T10:54:58.532543Z","shell.execute_reply.started":"2024-06-23T10:54:58.511373Z","shell.execute_reply":"2024-06-23T10:54:58.531685Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"diagnostic\nNORM          9025\nLVH           2505\nLAFB          2434\nCRBBB         2092\nLAO/LAE       2010\n1AVB          2010\nNST_          1972\nCLBBB         1278\nNDT           1154\nIMI           1121\nRVH           1017\nIRBBB          926\nISCAL          594\nISC_,LVH       521\nLPFB           412\nILBBB          386\nASMI           357\nISCLA          319\nRAO/RAE        262\nIRBBB,NORM     242\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"for i in ecg_df_HTV[\"diagnostic\"].index:\n    if ((len(ecg_df_HTV[\"diagnostic\"][i].split(\",\"))) > 1) & (\"NORM\" in ecg_df_HTV[\"diagnostic\"][i]) :\n        k = ecg_df_HTV[\"diagnostic\"][i].split(\",\")\n        k.remove(\"NORM\")\n        k.sort()\n        k = \",\".join(k)\n        ecg_df_HTV[\"diagnostic\"] = ecg_df_HTV[\"diagnostic\"].replace([ecg_df_HTV[\"diagnostic\"][i]],k)","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:54:58.533562Z","iopub.execute_input":"2024-06-23T10:54:58.533820Z","iopub.status.idle":"2024-06-23T10:54:59.424494Z","shell.execute_reply.started":"2024-06-23T10:54:58.533792Z","shell.execute_reply":"2024-06-23T10:54:59.423463Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"markdown","source":"Norm+","metadata":{}},{"cell_type":"code","source":"ecg_df_HTV = ecg_df_HTV.loc[ecg_df_HTV['diagnostic'] != \"\"]","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:54:59.426267Z","iopub.execute_input":"2024-06-23T10:54:59.426639Z","iopub.status.idle":"2024-06-23T10:54:59.491517Z","shell.execute_reply.started":"2024-06-23T10:54:59.426599Z","shell.execute_reply":"2024-06-23T10:54:59.490388Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"df = ecg_df_HTV.copy()\nfor i in ecg_df_HTV[\"diagnostic\"].value_counts().index:\n    if len(i.split(\",\")) > 2:\n        df = df.loc[df[\"diagnostic\"] != i]","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:54:59.493116Z","iopub.execute_input":"2024-06-23T10:54:59.493635Z","iopub.status.idle":"2024-06-23T10:55:32.193874Z","shell.execute_reply.started":"2024-06-23T10:54:59.493594Z","shell.execute_reply":"2024-06-23T10:55:32.192997Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"df[\"diagnostic\"].value_counts()[:20]","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:32.195150Z","iopub.execute_input":"2024-06-23T10:55:32.195478Z","iopub.status.idle":"2024-06-23T10:55:32.209537Z","shell.execute_reply.started":"2024-06-23T10:55:32.195450Z","shell.execute_reply":"2024-06-23T10:55:32.208487Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"diagnostic\nNORM        9025\nLVH         2506\nLAFB        2451\nCRBBB       2092\n1AVB        2045\nLAO/LAE     2010\nNST_        1972\nCLBBB       1278\nIRBBB       1168\nNDT         1154\nIMI         1121\nRVH         1017\nISCAL        594\nISC_,LVH     521\nLPFB         419\nILBBB        386\nASMI         357\nISCLA        319\nRAO/RAE      263\nIVCD         250\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"ecg_df_HTV[\"diagnostic\"].value_counts()[:20]","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:32.210782Z","iopub.execute_input":"2024-06-23T10:55:32.211115Z","iopub.status.idle":"2024-06-23T10:55:32.227693Z","shell.execute_reply.started":"2024-06-23T10:55:32.211088Z","shell.execute_reply":"2024-06-23T10:55:32.226744Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"diagnostic\nNORM        9025\nLVH         2506\nLAFB        2451\nCRBBB       2092\n1AVB        2045\nLAO/LAE     2010\nNST_        1972\nCLBBB       1278\nIRBBB       1168\nNDT         1154\nIMI         1121\nRVH         1017\nISCAL        594\nISC_,LVH     521\nLPFB         419\nILBBB        386\nASMI         357\nISCLA        319\nRAO/RAE      263\nIVCD         250\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"import random","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:32.228799Z","iopub.execute_input":"2024-06-23T10:55:32.229080Z","iopub.status.idle":"2024-06-23T10:55:32.235301Z","shell.execute_reply.started":"2024-06-23T10:55:32.229057Z","shell.execute_reply":"2024-06-23T10:55:32.234242Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"def Entering_missing_values(df,name):\n    df_numeric = ecg_df_HTV.select_dtypes(include=[np.number])\n    numeric_cols = df_numeric.columns.values\n    for i in df[name].value_counts().index[:25]:\n        df_1 = df[df[name] == i]\n        for col in numeric_cols:\n            if (np.mean(df_1[col].isnull()) > 0) and (np.mean(df_1[col].isnull()) < 1):\n                df_1[col] = df_1[col].fillna(df_1[col].median())                        \n        df[df[name] == i] = df_1\n    return df","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:32.236321Z","iopub.execute_input":"2024-06-23T10:55:32.236594Z","iopub.status.idle":"2024-06-23T10:55:32.247998Z","shell.execute_reply.started":"2024-06-23T10:55:32.236571Z","shell.execute_reply":"2024-06-23T10:55:32.247199Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV = Entering_missing_values(ecg_df_HTV,\"diagnostic\")","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:32.249005Z","iopub.execute_input":"2024-06-23T10:55:32.249286Z","iopub.status.idle":"2024-06-23T10:55:36.976468Z","shell.execute_reply.started":"2024-06-23T10:55:32.249264Z","shell.execute_reply":"2024-06-23T10:55:36.975650Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":"Информативность","metadata":{}},{"cell_type":"code","source":"num_rows = len(ecg_df_HTV.index)\nlow_information_cols = [] #\n\nfor col in ecg_df_HTV.columns:\n    cnts = ecg_df_HTV[col].value_counts(dropna=False)\n    top_pct = (cnts/num_rows).iloc[0]\n    \n    if top_pct > 0.40:\n        low_information_cols.append(col)\n        print('{0}: {1:.5f}%'.format(col, top_pct*100))","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:36.977568Z","iopub.execute_input":"2024-06-23T10:55:36.977875Z","iopub.status.idle":"2024-06-23T10:55:38.346343Z","shell.execute_reply.started":"2024-06-23T10:55:36.977832Z","shell.execute_reply":"2024-06-23T10:55:38.345384Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"0_VLF_Peak (Hz): 94.32928%\n0_LF_Peak (Hz): 86.06528%\n1_VLF_Peak (Hz): 94.12894%\n1_LF_Peak (Hz): 85.63399%\n2_VLF_Peak (Hz): 93.33315%\n2_LF_Peak (Hz): 84.66290%\n3_VLF_Peak (Hz): 93.39993%\n3_LF_Peak (Hz): 85.31957%\n4_VLF_Peak (Hz): 93.64757%\n4_LF_Peak (Hz): 84.81594%\n5_VLF_Peak (Hz): 93.86460%\n5_LF_Peak (Hz): 85.25279%\n6_VLF_Peak (Hz): 94.14842%\n6_LF_Peak (Hz): 85.85659%\n7_VLF_Peak (Hz): 94.29033%\n7_LF_Peak (Hz): 86.14875%\n8_VLF_Peak (Hz): 94.26807%\n8_LF_Peak (Hz): 86.20997%\n9_VLF_Peak (Hz): 94.51014%\n9_LF_Peak (Hz): 86.73864%\n10_VLF_Peak (Hz): 94.57970%\n10_LF_Peak (Hz): 86.73029%\n11_VLF_Peak (Hz): 94.47119%\n11_LF_Peak (Hz): 86.79151%\nsex: 72.38098%\n","output_type":"stream"}]},{"cell_type":"code","source":"def srez_df(ecg_df_HTV,name_column,count_v,count_n):\n    df = pd.DataFrame()\n    for i in ecg_df_HTV[name_column].value_counts()[0:count_v].index:\n         df = pd.concat([\n            df,\n            ecg_df_HTV[ecg_df_HTV[name_column] == i][:count_n]]\n        )\n    return  df","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:38.347442Z","iopub.execute_input":"2024-06-23T10:55:38.347755Z","iopub.status.idle":"2024-06-23T10:55:38.353268Z","shell.execute_reply.started":"2024-06-23T10:55:38.347727Z","shell.execute_reply":"2024-06-23T10:55:38.352273Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV[\"diagnostic\"].value_counts()[:15]","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:38.354450Z","iopub.execute_input":"2024-06-23T10:55:38.354755Z","iopub.status.idle":"2024-06-23T10:55:38.372919Z","shell.execute_reply.started":"2024-06-23T10:55:38.354729Z","shell.execute_reply":"2024-06-23T10:55:38.372127Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"diagnostic\nNORM        9025\nLVH         2506\nLAFB        2451\nCRBBB       2092\n1AVB        2045\nLAO/LAE     2010\nNST_        1972\nCLBBB       1278\nIRBBB       1168\nNDT         1154\nIMI         1121\nRVH         1017\nISCAL        594\nISC_,LVH     521\nLPFB         419\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"ecg_df_HTV = ecg_df_HTV.dropna()","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:38.374091Z","iopub.execute_input":"2024-06-23T10:55:38.374377Z","iopub.status.idle":"2024-06-23T10:55:38.452676Z","shell.execute_reply.started":"2024-06-23T10:55:38.374352Z","shell.execute_reply":"2024-06-23T10:55:38.451906Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV_diagnostic_n = srez_df(ecg_df_HTV,\"diagnostic\",15,20000)\necg_df_HTV_diagnostic_n.index = pd.RangeIndex(start=0, stop=len(ecg_df_HTV_diagnostic_n))\n","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:38.453766Z","iopub.execute_input":"2024-06-23T10:55:38.454054Z","iopub.status.idle":"2024-06-23T10:55:38.963503Z","shell.execute_reply.started":"2024-06-23T10:55:38.454031Z","shell.execute_reply":"2024-06-23T10:55:38.962392Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:38.964854Z","iopub.execute_input":"2024-06-23T10:55:38.965678Z","iopub.status.idle":"2024-06-23T10:55:39.003770Z","shell.execute_reply.started":"2024-06-23T10:55:38.965647Z","shell.execute_reply":"2024-06-23T10:55:39.002877Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"       0_Mean RR (ms)  0_STD RR/SDNN (ms)  \\\n0            0.294714            1.857784   \n1           -0.605783           -0.025090   \n2           -0.153063           -0.561229   \n3            0.388922           -0.576440   \n4            0.491271           -0.590281   \n...               ...                 ...   \n35934        1.832276           -0.345388   \n35935        1.192761           -0.575469   \n35936        1.054627           -0.579601   \n35937        0.724900            1.468744   \n35938       -1.434985            1.160155   \n\n       0_Mean HR (Kubios' style) (beats/min)  0_Mean HR (beats/min)  \\\n0                                  -0.448129              -0.339019   \n1                                   0.373440               0.335230   \n2                                  -0.083994              -0.126195   \n3                                  -0.515920              -0.547819   \n4                                  -0.586560              -0.616799   \n...                                      ...                    ...   \n35934                              -1.294939              -1.307278   \n35935                              -1.000112              -1.020355   \n35936                              -0.927215              -0.949222   \n35937                              -0.737068              -0.679312   \n35938                               1.583669               1.733434   \n\n       0_STD HR (beats/min)  0_Min HR (beats/min)  0_Max HR (beats/min)  \\\n0                  1.224009             -0.985329              0.333438   \n1                  0.010474              0.253886              0.160776   \n2                 -0.530526              0.203470             -0.348168   \n3                 -0.558183             -0.254884             -0.669709   \n4                 -0.568887             -0.331226             -0.720878   \n...                     ...                   ...                   ...   \n35934             -0.495041             -1.129730             -1.192945   \n35935             -0.572502             -0.779761             -1.016936   \n35936             -0.572380             -0.698416             -0.962449   \n35937              0.819327             -1.198591              0.199772   \n35938              2.066905             -0.353489              2.182334   \n\n       0_RMSSD (ms)    0_NNxx  0_pNNxx (%)  ...  11_Min_P  11_Max_Q  11_Min_Q  \\\n0          1.636349  0.698073     1.070923  ...  0.361770  0.032676  0.531560   \n1         -0.098543  0.983463     0.699654  ...  0.534591 -0.098155  0.298308   \n2         -0.520452 -0.728876    -0.859674  ...  0.044933 -0.359817 -0.148760   \n3         -0.528545 -1.014266    -1.156689  ...  0.390574 -0.464482  0.239995   \n4         -0.507999 -0.728876    -0.785421  ...  0.044933 -0.255152  0.239995   \n...             ...       ...          ...  ...       ...       ...       ...   \n35934     -0.335587 -0.728876    -0.414152  ... -0.081802 -0.302251  0.138919   \n35935     -0.493811 -0.728876    -0.573267  ...  0.281121 -0.302251  0.344958   \n35936     -0.494633 -0.443486    -0.274926  ... -1.435563 -0.197587 -0.545289   \n35937      1.796872  0.127293     0.560428  ...  0.027651 -1.448330 -0.961256   \n35938      1.195102  2.410412     1.367938  ...  0.309924  0.236772  0.232220   \n\n       11_Max_S  11_Min_S  11_Max_T  11_Min_T       age       sex  diagnostic  \n0     -0.868957 -0.188904 -0.414058 -0.374480  0.397948 -0.666883       CRBBB  \n1     -0.951303 -0.275921  3.169181 -0.231698 -0.081682 -0.666883       CRBBB  \n2     -0.292535 -0.000366  0.039331 -0.326886  0.136332 -0.666883       CRBBB  \n3     -0.374881  0.347704 -0.253178 -0.231698  0.136332 -0.666883       CRBBB  \n4     -0.292535 -0.000366 -0.019170  0.244242  0.136332 -0.666883       CRBBB  \n...         ...       ...       ...       ...       ...       ...         ...  \n35934  0.164485  0.544944  3.318360 -0.436352  1.051989 -0.666883        1AVB  \n35935 -1.441262 -0.600787 -0.124474  0.701145  0.485154  1.499483        1AVB  \n35936 -0.337826 -0.104787 -0.095223 -0.226939  0.179935 -0.666883        1AVB  \n35937  0.185071  0.176570 -0.238552 -0.598172  0.964784 -0.666883        1AVB  \n35938 -0.737204 -0.728412 -0.937649 -1.178819  0.833975 -0.666883         LVH  \n\n[35939 rows x 459 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0_Mean RR (ms)</th>\n      <th>0_STD RR/SDNN (ms)</th>\n      <th>0_Mean HR (Kubios' style) (beats/min)</th>\n      <th>0_Mean HR (beats/min)</th>\n      <th>0_STD HR (beats/min)</th>\n      <th>0_Min HR (beats/min)</th>\n      <th>0_Max HR (beats/min)</th>\n      <th>0_RMSSD (ms)</th>\n      <th>0_NNxx</th>\n      <th>0_pNNxx (%)</th>\n      <th>...</th>\n      <th>11_Min_P</th>\n      <th>11_Max_Q</th>\n      <th>11_Min_Q</th>\n      <th>11_Max_S</th>\n      <th>11_Min_S</th>\n      <th>11_Max_T</th>\n      <th>11_Min_T</th>\n      <th>age</th>\n      <th>sex</th>\n      <th>diagnostic</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.294714</td>\n      <td>1.857784</td>\n      <td>-0.448129</td>\n      <td>-0.339019</td>\n      <td>1.224009</td>\n      <td>-0.985329</td>\n      <td>0.333438</td>\n      <td>1.636349</td>\n      <td>0.698073</td>\n      <td>1.070923</td>\n      <td>...</td>\n      <td>0.361770</td>\n      <td>0.032676</td>\n      <td>0.531560</td>\n      <td>-0.868957</td>\n      <td>-0.188904</td>\n      <td>-0.414058</td>\n      <td>-0.374480</td>\n      <td>0.397948</td>\n      <td>-0.666883</td>\n      <td>CRBBB</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.605783</td>\n      <td>-0.025090</td>\n      <td>0.373440</td>\n      <td>0.335230</td>\n      <td>0.010474</td>\n      <td>0.253886</td>\n      <td>0.160776</td>\n      <td>-0.098543</td>\n      <td>0.983463</td>\n      <td>0.699654</td>\n      <td>...</td>\n      <td>0.534591</td>\n      <td>-0.098155</td>\n      <td>0.298308</td>\n      <td>-0.951303</td>\n      <td>-0.275921</td>\n      <td>3.169181</td>\n      <td>-0.231698</td>\n      <td>-0.081682</td>\n      <td>-0.666883</td>\n      <td>CRBBB</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-0.153063</td>\n      <td>-0.561229</td>\n      <td>-0.083994</td>\n      <td>-0.126195</td>\n      <td>-0.530526</td>\n      <td>0.203470</td>\n      <td>-0.348168</td>\n      <td>-0.520452</td>\n      <td>-0.728876</td>\n      <td>-0.859674</td>\n      <td>...</td>\n      <td>0.044933</td>\n      <td>-0.359817</td>\n      <td>-0.148760</td>\n      <td>-0.292535</td>\n      <td>-0.000366</td>\n      <td>0.039331</td>\n      <td>-0.326886</td>\n      <td>0.136332</td>\n      <td>-0.666883</td>\n      <td>CRBBB</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.388922</td>\n      <td>-0.576440</td>\n      <td>-0.515920</td>\n      <td>-0.547819</td>\n      <td>-0.558183</td>\n      <td>-0.254884</td>\n      <td>-0.669709</td>\n      <td>-0.528545</td>\n      <td>-1.014266</td>\n      <td>-1.156689</td>\n      <td>...</td>\n      <td>0.390574</td>\n      <td>-0.464482</td>\n      <td>0.239995</td>\n      <td>-0.374881</td>\n      <td>0.347704</td>\n      <td>-0.253178</td>\n      <td>-0.231698</td>\n      <td>0.136332</td>\n      <td>-0.666883</td>\n      <td>CRBBB</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.491271</td>\n      <td>-0.590281</td>\n      <td>-0.586560</td>\n      <td>-0.616799</td>\n      <td>-0.568887</td>\n      <td>-0.331226</td>\n      <td>-0.720878</td>\n      <td>-0.507999</td>\n      <td>-0.728876</td>\n      <td>-0.785421</td>\n      <td>...</td>\n      <td>0.044933</td>\n      <td>-0.255152</td>\n      <td>0.239995</td>\n      <td>-0.292535</td>\n      <td>-0.000366</td>\n      <td>-0.019170</td>\n      <td>0.244242</td>\n      <td>0.136332</td>\n      <td>-0.666883</td>\n      <td>CRBBB</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>35934</th>\n      <td>1.832276</td>\n      <td>-0.345388</td>\n      <td>-1.294939</td>\n      <td>-1.307278</td>\n      <td>-0.495041</td>\n      <td>-1.129730</td>\n      <td>-1.192945</td>\n      <td>-0.335587</td>\n      <td>-0.728876</td>\n      <td>-0.414152</td>\n      <td>...</td>\n      <td>-0.081802</td>\n      <td>-0.302251</td>\n      <td>0.138919</td>\n      <td>0.164485</td>\n      <td>0.544944</td>\n      <td>3.318360</td>\n      <td>-0.436352</td>\n      <td>1.051989</td>\n      <td>-0.666883</td>\n      <td>1AVB</td>\n    </tr>\n    <tr>\n      <th>35935</th>\n      <td>1.192761</td>\n      <td>-0.575469</td>\n      <td>-1.000112</td>\n      <td>-1.020355</td>\n      <td>-0.572502</td>\n      <td>-0.779761</td>\n      <td>-1.016936</td>\n      <td>-0.493811</td>\n      <td>-0.728876</td>\n      <td>-0.573267</td>\n      <td>...</td>\n      <td>0.281121</td>\n      <td>-0.302251</td>\n      <td>0.344958</td>\n      <td>-1.441262</td>\n      <td>-0.600787</td>\n      <td>-0.124474</td>\n      <td>0.701145</td>\n      <td>0.485154</td>\n      <td>1.499483</td>\n      <td>1AVB</td>\n    </tr>\n    <tr>\n      <th>35936</th>\n      <td>1.054627</td>\n      <td>-0.579601</td>\n      <td>-0.927215</td>\n      <td>-0.949222</td>\n      <td>-0.572380</td>\n      <td>-0.698416</td>\n      <td>-0.962449</td>\n      <td>-0.494633</td>\n      <td>-0.443486</td>\n      <td>-0.274926</td>\n      <td>...</td>\n      <td>-1.435563</td>\n      <td>-0.197587</td>\n      <td>-0.545289</td>\n      <td>-0.337826</td>\n      <td>-0.104787</td>\n      <td>-0.095223</td>\n      <td>-0.226939</td>\n      <td>0.179935</td>\n      <td>-0.666883</td>\n      <td>1AVB</td>\n    </tr>\n    <tr>\n      <th>35937</th>\n      <td>0.724900</td>\n      <td>1.468744</td>\n      <td>-0.737068</td>\n      <td>-0.679312</td>\n      <td>0.819327</td>\n      <td>-1.198591</td>\n      <td>0.199772</td>\n      <td>1.796872</td>\n      <td>0.127293</td>\n      <td>0.560428</td>\n      <td>...</td>\n      <td>0.027651</td>\n      <td>-1.448330</td>\n      <td>-0.961256</td>\n      <td>0.185071</td>\n      <td>0.176570</td>\n      <td>-0.238552</td>\n      <td>-0.598172</td>\n      <td>0.964784</td>\n      <td>-0.666883</td>\n      <td>1AVB</td>\n    </tr>\n    <tr>\n      <th>35938</th>\n      <td>-1.434985</td>\n      <td>1.160155</td>\n      <td>1.583669</td>\n      <td>1.733434</td>\n      <td>2.066905</td>\n      <td>-0.353489</td>\n      <td>2.182334</td>\n      <td>1.195102</td>\n      <td>2.410412</td>\n      <td>1.367938</td>\n      <td>...</td>\n      <td>0.309924</td>\n      <td>0.236772</td>\n      <td>0.232220</td>\n      <td>-0.737204</td>\n      <td>-0.728412</td>\n      <td>-0.937649</td>\n      <td>-1.178819</td>\n      <td>0.833975</td>\n      <td>-0.666883</td>\n      <td>LVH</td>\n    </tr>\n  </tbody>\n</table>\n<p>35939 rows × 459 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n# Круговая диаграмма, секторы которой будут упорядочены и расположены против часовой стрелки:\nlabels = ecg_df_HTV_diagnostic_n.diagnostic.value_counts().index\nsizes = list(ecg_df_HTV_diagnostic_n.diagnostic.value_counts())\nfig1, ax1 = plt.subplots()\nax1.pie(sizes, labels=labels, autopct='%1.1f%%')#autopct='%1.1f%%'\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:39.004954Z","iopub.execute_input":"2024-06-23T10:55:39.005345Z","iopub.status.idle":"2024-06-23T10:55:39.299872Z","shell.execute_reply.started":"2024-06-23T10:55:39.005320Z","shell.execute_reply":"2024-06-23T10:55:39.298889Z"},"trusted":true},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAcMAAAGFCAYAAACBh5ivAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACn/ElEQVR4nOzdd3jT5drA8W9Gk7Tp3hNaKHvvvZciQ8QBzjL06NHj696T4154jqIelaU42KhMQdkIyN60BUr3btORnfzePyqF2pautOl4PtfVS5v8xp2W5s6z7kcmSZKEIAiCILRgcmcHIAiCIAjOJpKhIAiC0OKJZCgIgiC0eCIZCoIgCC2eSIaCIAhCiyeSoSAIgtDiiWQoCIIgtHgiGQqCIAgtnkiGgiAIQosnkqEgCILQ4olkKAiCILR4IhkKgiAILZ5IhoIgCEKLJ5KhIAiC0OKJZCgIgiC0eCIZCoIgCC2eSIaCIAhCiyeSoSAIgtDiiWQoCIIgtHgiGQqCIAgtnkiGgiAIQosnkqEgCILQ4olkKAiCILR4IhkKgiAILZ5IhoIgCEKLJ5KhIAiC0OKJZCgIgiC0eCIZCoIgCC2eSIaCIAhCiyeSoSAIgtDiiWQoCIIgtHgiGQqCIAgtnkiGgiAIQosnkqEgCILQ4olkKAiCILR4IhkKgiAILZ5IhoIgCEKLJ5KhIAiC0OKJZCgIgiC0eCIZCoIgCC2eSIaCIAhCiyeSoSAIgtDiiWQoCIIgtHgiGQqCIAgtnkiGgiAIQoundHYAgtAUSZJETrEZncFCodFKkdFKobHk/wv++m+h0YrBYkWSQCYDkAFX/v/Kd+CikOOpUeLp6lLypXHB01WJp8YFr2u+l105URAEhxPJUBAqYLLaSMkzkJpvJCVfT0q+8a/vDaTkG0jXGTHb7A0Wj4tCRpCnhlAvV0K8NYR6uxLh40Yr35KvUG8NSoXo6BGE2pJJkiQ5OwhBcKbkPD1n0wo5l1bAufRCzqYXkJBdjL0J/WUo5TIi/bV0CvGkU4gHnUM86RziSaCnxtmhCUKTIJKh0KJczinmwKVcTibrOJdekvwKjVZnh1Vv/N1VdPorMXYO9aRPax/CfdycHZYgNDoiGQrNWlxGIQcu5XLgUi5/XsolvcDo7JCcLszblYFt/BjQxpdBbfyI8BXJURBEMhSalUvZxew4n8mBi7n8mZBLTrHZ2SE1emHergyI8mVgGz8GR/uJlqPQIolkKDRpkiRxJDGfrWcy2HomnQtZxc4OqcnrGOzB+C7BTOgSRJdQL2eHIwgNQiRDockxWmzsjc9m65kMtp3NJLvI5OyQmq1wH1fGdQ5iQpdg+kX6opCL5R1C8ySSodAkWGx2tp/LZN2xFHacz0Jvtjk7pBbHV6tiTMdApvQMZWi0v1j3KDQrYmGS0KjFJWfw8rpT9H9zGw98e5iNJ9NFInSS3GIzKw8nc8/Cgwx9dzsfbY0lKVfv7LCqJSYmBplMxjvvvFPm8XXr1pVJ6jabjfnz59OtWzc0Gg0+Pj7ceOON7N27t8x5S5YsQSaTIZPJkMvlhISEcMcdd5CYmFjmuJEjR1Z4X4CbbroJmUzGa6+95rgXKtSaSIZC46PPhT8WwIKBaDY/zrf7L5Ontzg7KuEaKfkG/vtbHMPf387ML/ez5kgyRkvj/pCi0Wh49913ycvLq/B5SZKYMWMG8+bN4//+7/84e/YsO3bsICIigpEjR7Ju3boyx3t6epKWlkZKSgqrV6/m/Pnz3HbbbeWuGxERwZIlS8o8lpKSwm+//UZISIijXp5QRyIZNjIxMTHcfPPNpd9/+OGH+Pj4YDSWXxKg1+vx9PTkv//9LwCRkZF8/PHH5Y577bXX6NmzZz1F7ECXdsGK++DDDrDlBcg6S3j674RoxIzQxkqS4I+LOTyx4jj93tjG82tOcipF5+ywKjR27FiCg4N5++23K3x+xYoVrFq1im+++Ya5c+cSFRVFjx49+PLLL5kyZQpz586luPjqBC2ZTEZwcDAhISEMHjyYOXPmcPDgQQoKCspcd9KkSWRnZ5dpXS5dupTx48cTGBhYPy9WqDGRDBu5e+65h+LiYtasWVPuuVWrVmE2m7n77rudEJmD2G1wchX8bwQsnQxn1oHtavKTWQ08HXbaefEJ1VZosvLDwUQmfbKHO/73B7+dzaAxTUlQKBS89dZbfPLJJyQnJ5d7/vvvv6d9+/ZMnjy53HNPPvkkOTk5bN26tcJrZ2ZmsnbtWhQKBQqFosxzKpWKu+66i8WLF5c+tmTJEmbPnl3HVyQ4kkiGjVxgYCCTJ09m0aJF5Z5btGgRN998M76+vk6IrI5MRfDHZ/CfnrB6DqQdq/TQ8ebfGiwswTEOXMplztJDjJu/ix8PJmKyNo4u1GnTptGzZ09effXVcs/FxsbSqVOnCs+78nhsbGzpYzqdDnd3d7RaLUFBQWzfvp2HH34YrVZb7vzZs2ezYsUKiouL2bVrFzqdjkmTJjnoVQmOIAp1NwFz5sxh0qRJXL58mdatWwNw8eJFdu3axZYtW5wcXQ0VpsOBL+DQYjDmV+sU96wjjPDLY2eOT/3GJjhcfGYRz605yQe/xnLvoNbcM7A1PlqVU2N69913GT16NE899VS552rSkvXw8ODIkSNYLBY2bdrEd999x5tvvlnhsT169KBdu3asWrWK7du3c88996BUirffxkS0DJuACRMmEBoaWq6bJSIigjFjxpQ59tlnn8Xd3b3M11tvvdXQIZeXHQfrHoaPu8Ge+dVOhFc86vtn/cQlNIjsIhMfbY1l8Du/89rPp8kqdN7a0OHDhzNhwgSef/75Mo+3b9+es2fPVnjOlcfbt29f+phcLic6OppOnTrxxBNPMHDgQB566KFK7zt79mwWLFjAqlWrRBdpIySSYROgUCi47777WLJkCZIkYbfbWbp0KbNmzUIuL/srfPrppzl27FiZrwcffNBJkQMFafDzo7BgABxbVmY8sCZ65m1BIWu4LZOE+mGw2FiyL4ER72/n/S3nKDA6Z5bwO++8wy+//MIff/xR+tiMGTOIi4vjl19+KXf8hx9+iJ+fH+PGjav0ms899xzLly/nyJEjFT5/5513cvLkSbp27Urnzp3r/iIEhxLt9CZi9uzZvP322/z+++/Y7XaSkpKYNWtWueP8/f2Jjo4u85hTxhSNupIW4P4vwGqo8+UURWk8GJ7IgqTIuscmOJ3ebGPB9gss25/IgyPaMmtIJBoXRdUnOki3bt246667SmdiQ0kyXLlyJffddx/vv/8+Y8aMoaCggAULFvDzzz+zcuXKCscDr4iIiGDatGm88sorrF+/vtzzPj4+pKWl4eLiUi+vSagb0TJsItq2bcuIESNYtGgRixcvZuzYsaXjh42K1QT7PoH/9ChJhg5IhFfcqdrtsGsJjYPOYOHdzecY/t52vt1/GUsDbpg8b9487Par95PJZKxYsYIXXniB+fPn06FDB4YNG8bly5fZsWNHmSVPlXn88cfZsGEDBw8erPB5b2/v6yZUwXlEObZGJiYmhsuXLzN//vwyj/v5+bFz507uv/9+oGTM8I477ihzTGRkJI899hiPPfZYmcdfe+011q1bx7Fjx+ovcLsdjv8AO94GXVK93EJSahhq/ZwUo7peri84XytfN565oQOTuoc6OxShhREtw0Zox44d9OrVq8zX66+/zvTp01Gr1bi5uVXrU2qDubQbvhgKP/2z3hIhgMxq5Bmx5rBZS8zV88j3R7n76wNcyCpydjhCCyJahkLt6XPh15fg2HcNdsti/x50SX62we4nOI9KIWfusCj+NbodrqqGG08UWiaRDIXaOf4jbHkR9NkNfuvZ2gX8LtYcthhh3q68Orkz47sEOzsUoRkTyVComZwLsOEJuLjDaSEcjbiPaXETnHZ/wTlGdwzk9SldiPB1c3YoQjMkkqFQPTYL7P0Ydn0A1vJFwxs0FG0QHfM+wmIX++m1NGqlnKfGd2DO0CjkYqNhwYFEMhSqlvQn/PwvyKq4OoczfBT4Bv9NbOPsMAQn6Rfpwwe39aC1n1imIDiGmE0qVM5mhe1vwaIJjSoRAsxw2ePsEAQn+jMhjxv/s5tv/0hwdihCMyFahkLFci/C6vsh5ZCzI6mQpFAzzPYFyWLNYYs3qkMA793agwAP8W9BqD3RMhTKO/Y9fDGs0SZCAJnNxLNhJ50dhtAIbD+fxY3/2cVvZzOcHYrQhImWoXCVuRjWPwEnfnR2JNWi9+9O5+TnnB2G0IjEDI7kxZs64aIQn/OFmhH/YoQS6adKdptvIokQwC37BOP8c50dhtCILNmXwJ1f7Sez0LkznoWmRyRDAY58A1+PgZw4Z0dSY4/4HnB2CEIj82dCHpP+u4dDCeKDklB9Ihm2ZHYbbHquZNmEk9cO1lbXnC2o5WKfQ6GszEITM7/az5K9l5wditBEiGTYUhkL4Pvb4cDnzo6kThTFmfwzPMHZYQiNkMUm8dovZ3h8+TGMFpuzwxEaOZEMW6LcS7BwHMRvc3YkDnGHyy5nhyA0YmuPpjDts30k5eqdHYrQiIlk2NIk7IWvRkPWOWdH4jBB6TuIdG2a3bxCwzibVsC0z/ZyIjnf2aEIjZRIhi3JkW/gm6lgaF4TC2Q2M8+EnXB2GEIjl11kZsaX+9l+PtPZoQiNkEiGLYHdXrLd0s//ArvF2dHUi1HG5tHlK9QvvdnG/UsPseJQ/W1CLTRNIhk2dzYrrLkf/vjU2ZHUK9fsU9wY0PB7KwpNj9Uu8cyqE/xnW9NbSiTUH5EMmzOrCVbcC6dWOTuSBvFPb7HmUKi++dtieX7NSWx2UYRLEMmw+TLr4YcZcH6DsyNpMF3EmkOhhn44mMg/vj0kll4IIhk2S6ZCWDYdLvzu7EgalFyfzSPhYpG1UDPbzmZy/zciIbZ0Ihk2N/pcWDoFEvc5OxKnuF2509khCE3Q7rhskRBbOJEMm5OiTFgyCVKPODsSpwlM30kbN7HmUKi53XHZPPDtYZEQWyiRDJuLgjRYPBEyTzs7EqeS2S08E3rc2WEITdSu2CyREFsokQybA0MefDutSe46UR9GGLY6OwShCbuSEE1WkRBbEpEMmzqzHr67HbLOOjuSRsM15ww3iTWHQh3sis3igW9EQmxJRDJsymyWknWEyQedHUmj85D3fmeHIDRxO2Oz+L8fjmEX6xBbBJEMmypJgnUPQbzoEqxI5+wtuCrEp3qhbjafTmfe+jPODkNoACIZNlWbn4OTK50dRaMlN+Twr/CLzg5DaAaW7Evgq13i31JzJ5JhU7TzfTjwhbOjaPRuVYg1h4JjvLXpLL8cT3V2GEI9EsmwqTm0GLa/4ewomoSA9F200xqcHYbQDEgSPLnyOPsv5jg7FKGeiGTYlMRvgw1POjuKJkNmt/J0iFhzKDiG2WrngW8OEZtR6OxQhHogkmFTkR0Pq2aDJCaF1MRw/a/ODkFoRgqMVmIWHSSzQFQ5am5EMmwKjLqSHSiMOmdH0uRocs8xJVDsbC44TqrOyMPfH8FiEzukNCciGTZydsmOZe2DorpMHTzoJfY5FBzrz4Q83twgCl00JyIZNnKfHv2U2RoDWZ7Bzg6lyeqYvQWtQnyKFxxryb4EfjqW4uwwBAcRybAR2564na9Pfs2xggvcERbMsYiezg6pSZIbcnk0PN7ZYQjN0PNrTnI+XUyoaQ5EMmykEgsSeXHPi0iUlILKMuYyW1XIqi7jnBxZ03SLWHMo1AO92caDyw5TYLQ4OxShjkQybISMViOP7XiMQkvZT5wWu4XX9ed5vfdNWBQqJ0XXNPmn76aju97ZYQjN0KXsYp5YfhxJEjVMmzKRDBuh+YfnE5dX+YSZVXknmd11iBhHrAGZ3cpTwcecHYbQTG07m8HnOy84OwyhDkQybGT2pezjh3M/VHncsYILzAgL4XhEjwaIqnkYphdFzYX6M39rLCeTxfKnpkokw0ZEZ9Lx8t6XS8cJq5JpzGGWqkiMI1aTOvc8twSJNYdC/bDYJB5fcQyjRRTGaIpEMmxEXv/jdTINNXuzvjKOOE+MI1bLA55/ODsEoRmLzyzivc3nnR2GUAsiGTYSP1/4ma2Xa9+Nt/KvccRsjyAHRtX8tM/6Fa1SfHIX6s/ifZfYdyHb2WEINSSSYSOQWpTK2wfervN1jhVc4I7wUDGOeB1yYx6PizWHQj2SJHh65QkKxXKLJkUkQyezS3Ze2PMCRZYih1zvyjji6i5jHXK95miabIezQxCauZR8A6/9fMbZYQg1IJKhk31z+hsOZxx26DUtdguv6WP5d++bsMhdHHrt5sA3fQ+dxJpDoZ6tPpLM5lPpzg5DqCaRDJ0orSiNz45/Vm/XX5F3kjndh4lxxL+RSTaeDjnq7DCEFuDln06J7tImQiRDJ3r3z3cxWOt3J/ajungxjliBoUVin0Oh/mUVmvhoa6yzwxCqQSaJGkJOsSdlDw9te6jB7ucid+FFTRTTT29rsHs2dk97f8TK9JZRxafw6EYKj27EqssAwMW/Fd6DZ+Lati8AktVM7u8L0Z/dhWSz4BrVG9/xD6HQ+lR6zcvvTqrwce+Rs/AaMB3JaiFn83/Rx+1HofXBd/w/cY3sWXqc7sBqbAVZ+I570HEvtBFSyGWs/9dQOoV4OjsU4TpEMnQCs83MtJ+mkViY2OD3vt2nG88d+xUXe8N03djsEq/tMLHspIX0IolQDxkxPVS8NFyFTCar8JwdCVZGLS0/ppf2pDvB7iWdGd+dsPDcb0aKzBKzeqr4aIKm9LiEfDvjv9Vz6AEtnuqK7wFwPuI2JsRNq+MrbBr08QeQyeQofUIBKDr1GwUH1hAS8x9UAa3J2bIAw4VD+N30GHK1ltytnyOTyQm++/1Kr2kryivzveHiIXI2/ZfQf3yFi3cwBYd/oejoRvynPofh4mEKDq4m/JFlyGQyLPnpZK54hZD7PkaudqvX194Y9G3tw8oHB1X6b15wPqWzA2iJFp5a6JRECCXjiHHdh/HRhdP4F2bU+/3e3Wvm80MWlt6soUuggkOpNmb9ZMBLA48OUF/33POPlE1mgdqS/8/W25n7i4ElU11p4yPnpu/1jI5SMKl9yWShf24w8s5Y9XUTIUD7zF/xUE6m0Nr8/wzcogeU+d5n+L0UHd2IKfU8Sk9/ik5sxX/yU7i2LulO95/4GKlfP4Qp5RzqsI4VXlPhXrbVqI8/gKZ1N1y8S1rblpwkXKMHoApojdI7mPwdi7AbClC4eZH762f4jIxpEYkQ4NDlPFYdTua2vhHODkWohBgzbGDJhcksOrnIqTFcGUc8EV7/44j7kmxM7aDkpvYuRHrLubWzC+PbKjmYUvVmu4FaOcHuV7/kf32qvpgn4aWWcUdXF/qFKRgVpeBsVsn1fjhpwUUBt3SqehatzKTjifDKC6I3V5LdRvGZndgtRtRhHTGlx4PdWqYL08UvAoVnAKbUc9W6pq04D8OFP3HvPr70MVVgFKbkM9gtJoyXjqBw90Xu6knR6e3IlCrc2g929Etr1N7dfA6dQUymaaxEMmxg7xx8B6PN6OwwStYjqotZ07l+1yMOjlDw2yUrsTklVV+Op9vYk2jjxuiqW2M9vygi5MNCxn1bzN5Ea+nj7Xzl6C0SR9Ns5Bok/kyx0T1IQZ5B4uXtRj69UXOdq5Y1lR01fk1NlTkrgcSPbiXxg2nk/PoZgdNeROXfCntxHiiUyDXuZY5XaL2xFedVcrWyik79hlzlWibBuXcbh0tgFKkL/4nujxX4T30Wu7EI3Z7v8B37D/J2fUvK/+4nY/nLWAubf8WW7CIzH2wRpdoaq+bfP9SI7Ejawc7kxrPJrNlu5lVDLGd638Sz9TSO+NxQFQUmiY6fFqOQg80Ob45Wc1f3yltuIe4yvrhJQ99QBSabxNdHLIxcqufAXC29QxT4uMpYerMr964zYLBI3NvDhQnRSub8ZOCR/iou5duZ8qMeiw1eG6nm1s6V38snYy/dPO7lZKHW4a+9sXHxDSNk1n+xm/Toz+8he8N8gu58xyHXLjqxDW3nkciUV+vjyhRK/MaXnSSWveFjPPpMxpxxEUPcH4TM+oSCA6vJ2/YlAdNecEgsjdl3By5zR78IuoZ5OTsU4W9EMmwgNruNjw5/5OwwKrQ87ySx9TSOuOK0le9OWvh+uitdAuQcS7fx2BYToR4y7utZcWHxDv4KOvgrSr8fHKHkQp6d+fvNfDvNFYBpnVyYdk1X6M4EKycybXwyUUP0f4v4Yborwe4y+n9dzPDWCgK1FXeCyCQ7TwUf4b7CYQ581Y2TTOGCy18TaNTB0ZjT4ig89DNunYaBzYrdWFSmdWgrzr/ubNIrjEmnsOYm4z71mesfd/kElpzL+N34L/K2L8K1TV/kKg1uHYeS8f36ur24JsIuwdubzvLd3IHODkX4G9FN2kDWX1zPJd0lZ4dRqZJxxDCHjyM+vdXIc0PUzOjqQrcgBff0UPH4QBVv7zHX6Dr9QxXE51Y8zmiySvxzo5H/TXIlPteO1Q4jIpV08FfQ3k/OgeTrF+YeVNgy1xxKkoRks6AOjga5EsPl46XPWXKSsRVkoQ6tePLMtYpObEUVHI0qsE3l97Kayd36OX4THkEmV4BkR7L/9Xux25CkqseQm4u98TnsjstydhjC3zTaZBgTE8PNN9983WOSk5NRqVR07dq1wudlMlm5r6FDh1b4vFKppFWrVjzxxBOYTCZHvhQsdgufH//codesD5nGbIePI+otIP/bpE6FrOQTck0cy7AR4l7x7NA3dpm4oa2S3iEKbHawXnNxiw1sVdxLlX+BGSFpNQuoicnbuaSkBafLwJyVQN7OJZgST6LtPBK5Wot793Hk/f41xssnMKXHk7PxY9ShHcvMJE356kH0sfvKXPdKl+u1E2cqkr/vR1zb9EUV1BYAdVhn9LH7MGdeovDIejRhnRz/ohuxdzefQ6xqa1yadDfpkiVLuP3229m1axcHDhxgwIAB5Y5ZvHgxN9xwQ+n3KpWqwuctFgvHjx9n1qxZaLVa/v3vfzsszjWxa0gpSnHY9eqTo8cRJ7dX8uZuE628ZHQJVHA0zcZH+83M7nm1i/P5bUZSCiW++asL9OP9JqK85XQJVGC0lowZ/n7Jxq93l5+GfybLxvLTVo7+o2TMr6N/yazThUfMBLvLOJdtp1+ootx5fzfH/Q9+5JY6vdbGzFasI3v9R9iKc5GrtagCIgm8fR6uUb0A8B1zP7kyOVnr3kKyWdBE9cZv3D/LXMOam4zdVHb9Z/HZXSCBtvOISu9tzkpAf243ITGflD7m1nEIxqSTpH/3LC5+YfhPftqBr7bxO5VSwC8n0pjSI9TZoQh/abSL7mNiYsjPz2fdunUVPi9JEtHR0Xz22Wds376d3NxcvvzyyzLHyGQy1q5dW2kLs6Ln586dS1paGhs2bHDI6zDZTExcPbHGm/Y2Br29ovkw/hT+RbWPvdAk8fJ2E2vPWcgsLll0P7OrC6+MUKNSlLT0YtYZSMi3syOmJKG9t9fEl4fNpBRKuLnI6B4k55XhakZFlf3sJkkSwxbreW6oqnSNIcD6WAsPbzRissIbo9XM7V31pseS2pOe+k/RWZr050OhCWkToGXr4yNQ/L3rRHCKJvuXv337dvR6PWPHjiUsLIzBgwczf/58tNrazwqMjY3l999/JyYmxmFx/njuxyaZCAGO6OK5IyKcjwuD6ZZ8olbX8FDL+PgGDR/fUPlyhyU3u5b5/pkhap4Zcv0F+VDyYWbP7PK/70ntXcokx+qQmQp4IjyWVy91rtF5glBbF7OKWXc0hel9wp0dikAjHjOsysKFC5kxYwYKhYKuXbvSpk0bVq5cWe64mTNn4u7uXvr195bmlec1Gg0dOnSgS5cuPP/88w6JUW/Rs+iUcxfY11WmMZsYtZ619bwesTGYIm13dghCC/Pf3+Ow2lrO5KHGrEkmw/z8fNasWcPdd99d+tjdd9/NwoULyx07f/58jh07Vvo1bty4Cp8/fvw469evJzY2lnvuucchcX575ltyjbkOuZYzme1mXjHE8kav5r0/onfGH/T0dMwmy4JQHZdz9Kw50jTmEzR3TbKb9Pvvv8doNJaZMCNJEna7ndjYWNq3b1/6eHBwMNHR0ZVe69rnO3ToQGFhITNnzuSNN9647nlVKTIXsfTM0lqf3xgtzy+pa1rXccTGSibZeTLoCPcUDHd2KEIL8uXui9zWN1wU8XayJtkyXLhwIU8++WSZFt/x48cZNmwYixbVrVtSoSiZeWgw1G2fwVWxqyg0F9bpGo3RlXHEk+HdnR1KvRhY0DLXHArOE59ZxI7zYt2hszXqlqFOp+PYsWNlHissLOTIkSN89913dOxYdkHwzJkzmTdvHm+88QZKZfVeWn5+Punp6djtduLi4pg3bx7t27enU6far3uy2C0sO7us1uc3diXjiCpe6jyWaWea1/6ILrqL3BWSyndpYsq70HC+2n2RUR0DnR1Gi9aoW4Y7duygV69eZb4WLVpE586dyyVCgGnTppGZmcnGjRurfY9Zs2YREhJCeHg4M2fOpEuXLmzatKnaybQiWxK2kKGv/+2RnKl0HLF38xtHnK3dV/VBguBA+y7kcDpV5+wwWrRGu86wKbv9l9s5m3vW2WE0GEesR2xMJLUHvfULyBNrDoUGNK1XGPPv6OnsMFqsRt0ybIoOpR9qUYkQSsYRZ7RqPuOIMlMhT0aIrXaEhrX+RCrpOudv79ZSiWToYN+f+97ZIThFhqF5rUecZP/d2SEILYzFJrF4X+Mt5t/ciWToQOnF6WxPbLkLt5vTOKJX+n56e4k1h0LD+v5AIsUma9UHCg4nkqEDrTi/Aqsk/iEvzzvJ3O7DyHZvurPjZEg8GXjY2WEILUyh0cqGk817B5XGSiRDB7HarayOW+3sMBqNK+OIp8K6OTuUWutfsAWZTMwvExrWqkPJzg6hRRLJ0EH2pe5rFqXXHCnDkM19GkOTHUd00SVwb0iqs8MQWpiDCbkkZBc7O4wWRyRDB/nlwi/ODqFRujKO+GbvSU1yHDHGba+zQxBaoFWHReuwoYlk6ABF5iJ2JO1wdhiN2o95J5jbfRg57gHODqVGIjO34qeq2wbHglBTq48kY7eLLvqGJJKhA2y9vBWjTawPqsoRXTx3tIpoUuOIMnMxT4afc3YYQguTpjOyJz7b2WG0KCIZOsD6i+udHUKTcWUccV0TGke8ySbWHAoNb6XoKm1QIhnWUXpxOocyDjk7jCbFbDfzsiGWt3pNwipv/CXPPDMO0ter+e1AIjRuv55OR2cQXfQNRSTDOtpwcQN2SexUXRs/5J9gbvcRjX4cUaw5FJzBZLWz5XS6s8NoMUQyrCPRRVo3h3VxTWIcsZ9us1hzKDS4X083791vGhORDOsgNi+W+Px4Z4fR5GUYsolxNTbqcURlQSIxIWIMR2hYe+KzMJhtzg6jRRDJsA52Ju10dgjNhslmavTjiPe5iX0OhYZltNjZFZfl7DBaBJEM62B3ym5nh9DsNOZxxNYZ2wgQaw6FBia6ShuGSIa1pDPpOJF1wtlhNEtXxhFPN7JxRJmlmKfCW9ZelYLz/X4uA5tYgF/vRDKspX2p+7BJoi+/vmQYsrnP1chPncY4O5QybhRrDoUGlqe38GeCqHtc3xrn4EwTsDu56XWRSnaJzLWZ5P+Rj1VnRemtxGeoDwFTApDJZBWeUxxbTMaKDExpJuxmOy5+LviO8sV/gn/pMfn78klflY7daMdnmA8hM0NKnzNnmUn4IIG2r7VF4aqoUbwmm4mXbHGc6T2Jp49tRml3/vZYHhl/MsC7gAP5ns4ORWhBfj2dwcA2fs4Oo1kTLcNakCSJvalNr4Bz1oYscrfnEnp3KO3eakfw7cFkb8omd1vlnzrlajm+Y32JeiGKdm+1I3BKIBmrM8jdUXKOtdBKyuIUQu4IIfKpSPL35VNwrKD0/NRvUwm6LajGifBa3+c1nnFEGRJPBIgiC0LD2nZWjBvWN5EMa+FU9qkmuV2TId6ARy8PPHp6oApQ4dXPC/cu7ugv6is9x7W1K94DvdGEaVAFqPAe7I1HNw+Kz5dsMWPOMqNwVeA1wAu3Nm5oO2kxpZoAyN+fj0whw6uvV51jP6yLY0arVo1iHLFPvlhzKDSsxFw9KfkGZ4fRrIlkWAtNdRapa7QrxWeKMaWXJCtDooHiuGI8unlU+xqGywb0cXq0HbUAqIPU2M12DJcNWIusGC4Z0ERosBXbyFyTScjdIVVcsfrSDVmNYhxRWZjMnDCx5lBoWAcu5jg7hGZNJkmS+IhbQ3duuJOT2SedHUaNSXaJjFUZZG/KLvkYZIeg6UEETKq6+/Hc4+ewFdqQbBKBNwcSODWw9LmCwwVkrM1AMkt4DfIiaFoQyQuT0YRrcI10Je27tNLzvPrVvZUIcKdPd6eOIyaGT2Z4/Eyn3Ftome7oG8G7t3Z3dhjNlphAU0MGq4EzOWecHUat6A7qyN+fT/g/wtGEaTAkGkj/Pr10Is31tHmhDXajHf0FPRkrM1AFqfAe6A2AZx9PPPtcnVBSfK4YU7KJ0LtDiX02logHI1B6Kbkw7wLaDlqUnnX/Z/d93gliu4/gw7jj+BY3/FY3ERm/Eai+lUxT09uwWGiaDlwSLcP6JLpJa+hU9qkmu6QifUU6ARMDSsYAIzT4DPHBb4IfWeurrnChClChidDgO9IXvwl+ZK7LrPA4u8VO6jephN4XijnTjGST0HbUog5Row5Wo79Q+fhkTR3SxXFH69acDu3qsGtWl8yi5+mw0w1+X6HlSsjRk1Eg9k2tLyIZ1tDxrOPODqHWJJNU7jcuk8ugph3lEkiWik/K+jkL927uuEa6ItkluGZDD8la9ntHSDdkcZ+biZ+dMI54g1WsORQa1n4xblhvRDKsoaZcdcajpwdZv2RReKwQc5aZgsMFZG/JLtPFmb4yneQvr04OydmWQ8HRAkzpJkzpJnJ35pK9KRvvwd7lrm9MMaI7qCPoliAA1CFqkEHuzlwKjxViSjPh2sbV4a/LZDPxojGOt3vd1KB1TT0yDzHER9dg9xOEA5ea3iz2pkKMGdZQU06GIXeHkLkmk9RvU7EWlCy69x3pS8DUqxNorPlWzDnmqydJkLEqA3OWGZlChipQRdDtQfiO9C1zbUmSSF2SSvDMYOTqks9YcpWcsLlhpH2bhmSRCLknBBef+htj+z7/ZIOPIz7m/yd78xrvbhtC8yJmlNYfMZu0BpILk7lxzY3ODkOoQrBrAB/nm+iSeqre72X1CKND9rvYJNHJIjSMY6+Mw9tN5ewwmh3xF1wDTblV2JJcGUf8pdPoer+XsjCF+8OS6v0+gnDF2bRCZ4fQLIlkWANNefJMS2OymXjBGM87DbA/4l2aPfV6fUG41tm0gqoPEmpMJMMaEC3Dpue7/BM80H0EuVr/qg+upfD03wnRmKs+UBAcQCTD+iGSYTVZ7VbO5513dhhCLfypi2NGPa5HlFkNYs2h0GDOpotkWB9EMqymxMJELHaxy3lTlVbP44jjzb/Vy3UF4e9iM4qw2hy8YFcQybC6LuVfcnYIQh3V5ziie9YRhvvmO/SaglARs9XOxexiZ4fR7IhkWE0XdBecHYLgIPU1jvh/fn869HqCUBkxbuh4IhlW00XdRWeHIDjQlXHEM6FdHHbNnnmbUchE95VQ/86IZOhwogJNNT3wv2Tu04VTHOJNdoCKy95WzrkXclSTQY7cccWnhYaTZsjiXjc1r3YazeSzda8zqihK4x9hiXyWHFn34AThOi5liW5SRxPJsJos52OhsBBtHGiB1sDwv56T+/thDg9EF+RGup+Ci15GTrnlcdIlA6toKTRqJpuJF2zxnOk9iScdsD/inZo9fEakY4IThEqIXe8dT5RjqwZrbi5xg4fU/EQXF2RhwehDfcgJ0JDsayfWvZBjmmySlaLAc2PTz6sdH9Sxrqmk1DDU+jkpRrUDIxOEsrzdXDj2ynhnh9GsiGRYDfqjR7k8806HXlPm5YktPIjCEE8y/BRc8jZzxi2fo+p0jDLn7N4uQIhrAB/nG+mcWvt1g+vCnuKxC70dGJUglHf69Qlo1aJzz1HET7IaLKmpDr+mpCtArivA6zR4Ae2BCQAKBbLgQExhfuQFupLiC3Geek5osolzERXr65sjxhHHmbcBIhkK9Ssl30D7IA9nh9FsiGRYDdasqneCdxibDSklDVVKGkFAECVvq3cAMq0We3gQxcFeZAW4cNnbyln3Ao6o09HJxQ7YjlI6jthrEk8er/k4ojbrGCN989iR61NPEQoCpOSJZOhIIhlWQ4Mmw+uQiouRnb+I+3lwB6KAkQAyGbJAfyxhAeQHupHmJ+eCl4GTrjmcUWVhq/FW9gLAsvwTxHYfyQdxR/Eprlmr/P/8DrIjd0I9RSYIkCwm0TiUSIbVYMtumI1ia02SkDKyUGZk4Q/4A92AmwGZWo0UFow+1JscfzVJPjbOexRxVJNBuqLIqWE3BQd1sdzROpKP84NrNI7YPXcLLvLxWOyyeoxOaMlS8kQydCSRDKvBmtXIk+F1SCYTXLyM28XLuAERwGBgFiDz8cYaHkRhsHvpkpAzWh3HVemYZDbnBt6IlNQ1VfNKDcYRFcXpPBiewCeJUfUcndBSieUVjiWSYTU0lm5SR5Py8lHk5eN9EryBjsBEAKUSWUgQxjBfcgNdSfaxE+tZzHFNFgnKfGeG7DTGWowjznTZzSeIZCjUj9xik7NDaFbE0opqiB00GFtenrPDaBRkHu7Yw4MoDPYiy19JgreFM+46jqkyKJS3jD/O/l7tqzWOKCnUDLN9QbJYcyjUg+7hXvz8yFBnh9FsiGRYBclq5Vy37iB+TNcnkyELDsQc6kd+kJZUX4j3NHDSLYezyiykZjZ0FuoayMd5ejqlnbnucT+HP8Gj8X0bKCqhJYn0c2PH06OcHUazIZJhFaxZWcQNG171gUKlZK4apPBgioO9yA5Qk+hj47y2kCOuGWTLm26NRY1CzavKCCadq3wcUe/fg87JzzZgVEJL4adVcfjlcc4Oo9kQY4ZVsBvEIHVdSQYjxCWUqes67K/n5P6+WMIC0QW7k+4r46KXmdPaXE66ZGJu5JN4jDYTz18ZRzy2CYVUPl637OOM889la7avEyIUmrNCo6hU5UgiGVbBbhCL2euTPTsXRXYuvsfBF+gMTIKSSTzhwRhCfMkJ1JRM4vEo4rhrNomKfKfG/Hff5p/gfI9RlY4jPuJzgK3ZNzohMqE5M9vsGC02NC4KZ4fSLIhu0ioYjh8n4Y4Zzg5DuIbM0xNbRBCFwR5k+iu55GXmjFbHMVU6ernFaXFVNo5o0wbSOe8jTHaxfajgWAdfGEOgp8bZYTQLomVYBdEybHykggLkp6/WdW0HjAeQy/+q6+pPXqArqX4Q56HnuFs2ccr6r+uaasjkXq2aVzuOLjOOqCjO5J/hCcxPbFPvMQgtS4HRSqCns6NoHkQyrILdKMYMmwy7HSk1HVVqemld117A7YDMza2krmuIN9n+LiT4WDmnLeCoJoM8ueN+x5WNI97usov5iGQoOFah0Xk9Ic2NSIZVkIyiZdgcSHo9sthLuMeW1HWN5K+6rlBa11UXpCXNV8YFLyMn3XI5rcqsdV3Xb/NPENtjFO//NY4YnL6DVq4zSDSILi3BcSw2McrlKCIZVkF0kzZ/UmY2ysxs/AA/oCswFZCpVEjhIRhCfMgOVJPkbSPWo4ijmkzSFIVVXveALpYZraP4OC+ITmlneDb8JA/H96vnVyO0JDa7SIaOIpJhFSRTy6iqIpQnmc1wMRHXi4lEcLWuawwldV1t4YEUBHuQ7qfgkpeJM9p8jv2truuVccTXOo5idNZWQCRDwXHsYv6jw4hkWBV5MyudIjiElJeP/G91XW+Eks2ZQ4MwhvqRG6ghxVcizkPPV67ZnGkdyVhzLtuyxJrD6zEmnaLgwGrMGRewFeUSMO1F3NoPqt65yWfI+P45XAJaEzrrk9LHi05vJ3/nUiSzAW23sfiOub/0Oasug4zlLxNy38fI1W4Ofz31SSRDxxHJsAoyhfgRCTVgsyElpaJOSiUECAH6yGRM6zma/M79iOzkwhS/OCxGBeoiLRqDBovSA7PCDTNqLFYFFouMlrwF5QlUxIV2IbLPLXyy4nlmqDzoo/Gr8rxiYyGvbfwPXdv0Q1eUy+t/nVOoz+eJzZ/wwNSXCfAJZf73TzGt3VB6ti+p6/nR6jeZNu5f9PWKqNfXVR/CbGK5jqOId/oqyJRNa0Hr2AvxpFrLV6aY6e3Ny0HB5R6PM5n4NDuL00YjqVYrzwUEcq9v2ZbLLwU65mdlobfbmeblxbOBQaXPpVjMzE1KYmXrSNwVTetnVZ/sGi36AZPIDe1HapEHyIvwMh8ilCh+jOjLlMu/4hW4iX1aX4zZdtpkKmmdasPrYib21CxsAWFY/cOweQVj8fDH4uqD2cUDk9wNk6TCaFFgNEgY9dZmVza3jUcP2nTuUfq9Mc9EUbq+yvMWbXuLPlGjkMnk5OXvLT3ncuZFNC5auvgPBqBdcA8SLsYR7dmbQ/G/I1lkdPTpX617NDYuYr9MhxHJsCrKpvUjWtE6kmuLgsWZTMxNTmKCh0eFxxvtdsJdVEzw8OSdzIxyz+dZrbySns5bwSGEu7jwUEoyA9y0jHR3B2BeRgZPBASKRAjY/MMo7DeFLJ/OpGUrsZrtkAZe/nkUpK9gcPd7uaDTM7TVBv4TMgOX491Y4LeKoPStHIrszaYegfxWbEBhVDO8WEPPbBOtUuPwObEHKTmtwntKCiU2/7CSL++/EqebL2bVlcSpxmhVYDSCsdiGvZlOuPjj3GZyCtK4b/QLbD6yrMxzgV5hWKwmkrLj8HUP4nLWeQZ2vAG9qZD1fy7m/yZ/6KSoHaCODcOYmBjy8/NZt25dueciIyO5fPkyAG5ubnTo0IHnn3+e2267DYDXXnuN119/vdx5W7duZezYseWe9/T0pHv37rzxxhuMGDGiboHXg6b1Tu8ETa2b1Pdvyfvr3BwiXFzo51rxWEg3V1e6uboC8FFWZrnnkywW3OVybvQsWdnb382NC2YTI3FnQ0EBSpmMcZUk2pbAEt2L/O43kOnSmswsG5IBMADYAfANziXr0o8oUOKe64FMqyfElEdkUA4XenRg5N4Y5oSM4PG8rxhw6U9ekivZH9WPza3UfOAbS2FUEQyBYJs3w4rD6JGtJTzZhPZCGlJ6JjKbFWXGZZQZl6uMVZLJsPuFYgsIx+oTgrU0cXpiUrhhQo3RosRoAkOxDXsTmbafqUvm54Nf8diUj1HIy38oc1N7cM+oZ/lm+7tYrCb6tx9H54h+fLfjfUZ0uZmcgnT+t/llbHYrE/veS682je+NujJyWf22DOfNm8f9999PQUEBH374IXfccQdhYWEMHlzSyu7SpQvbtm0rc47vNT1L1z6fm5vLBx98wKRJk0hOTsbLy6teY6+ppvVO7wRNrZv0WmZJ4peCAu7z8UFWyz+a1ioVRknijNFIqIsLp4xGbvHyQmez8d/sLJZEtHJw1I2bJFdg7DOevDZDSbMEoMu1QC5A+SLdAeFppJ5dic1qZXD/u5GybMiQkZEexI2Br/C0+2e0HRLGwr3wjTSP/7XZzZC0bxh64Q+GAhaFir1R/dnk6cmOwousVJxnpSfQBhgOrWz+DC0Mplu2G2FJBtziU7FnV15pRyZJKLJTUGSnoKrGa7X5BmHzj8DmE4zFMwCL1g+L2hOTQosJDUbrX4lTb8dmsdfuB1pHdruNJb+9xcS+MQR5Vz7m1yNqKD2iru79F5d6nJTcS9w25F+89uO9zBrzIp5uvry/9mGiQ7rj4erTEOHXmUJVv2OGHh4eBAcHExwczIIFC1i2bBm//PJLaTJUKpUEB5cffrni2ueDg4OZN28eixcvJjY2ln79GtfMapEMq9LEukmv9VthIYU2G9Pq8AnMS6Hg7eAQnk9LwyjZmeLpyVCtOy+lp3GXtw8pFgsPpyRjlSQe9vdngkfzqw1ld/eiuP8UcoJ7karTYiy2QgZA5dU/AiMukHjy55J9MGUyQqyRSFiQI+P8eRlDAnTMdjvAB9Z+9BoSxoW9KdwdN4K+Xr35PPBHAlK342IzMzJ+DyMBo4sru6P6sdldy+6CCxhsRhIV+XzvnV8ynTUaGAXRlkCGFobQOVtNaJIedVwyUl5+rV63IjcDRW5J17lrVT8jTz9sARFY/UKxegZg0fpiVnthVrhjkmkw2pQYTXIMeltJ97GDGC0GErPOk5wdx8o9/wVAkiQkJB79chwP3/QeHcJ6lTnHYjOzfM9/uG/Uc2QVpGC322gXWjJGGegVTkLGWbpFDnZYjPVJ2YBFupVKJS4uLpjN5lqdbzKZWLx4Md7e3nTo0MHB0dVd032nbyAypYuzQ6i1NTodw7RaAuv4GsZ6eDD2mq7QP/V6Yk0mXgwM4oaLF/kgNBR/pYI7Ll+mr6sbfk34A8QVtpAoCvpMJtOjA+nZ8pKWTyrA9bfNkZAIDD9J4omrXUfdO41ByitJnDJkWK2gUnWjp+5dumlXcRR7aUI8pPOgn+5+nmw1moeMX6IsSAJAYzEwLnYX4wC9SsvONv3Y7KZhjy4es/3qm1O8Sy7xvrklW4C0B8ZAZ3MIQwqC6JSlIiipCFVsElJh1UUDakJekIO8IAeXC8eqPNbu7oUtIAKbbygWr0AsWn8sGi/MSi0mmSsmmwsGsxyjoeqkqVG58cJtX5d5bPfpn4lNPcqcca/i51G+1bLlyHd0juhHREB7krLjsF+z9ZbNbsUuOaeVWxvKem4ZXmE2m/nwww/R6XSMHj269PGTJ0/i/tf8AYDOnTtz8ODBCp/X6/V4eHiwfPlyPD0b34fmWr1rpaen8+abb7JhwwZSUlIIDAykZ8+ePPbYY4wZM6bMwKurqytt27bl//7v/5g7d27pNXbs2MGoUVd3adZoNLRp04b/+7//44EHHih9PCYmhqVLl5Z+7+vrS79+/Xjvvffo3r176ePXdgMqFApCQ0O59dZbefvtt1Gr1bV5mQDI3ar6TNw4pVgs/KEv5j+hYQ69rtluZ15GOu+GhJJoNmNDop9byXhkpErFCaOBUe5NcwzR3Gkg+V3GkS4PJzvLCkWUfFG9N0eZTMIvaD9JJ/8o83h7737w1xu7XCr5d6rLb41ac4AY6Sue5n6Oaq4mRLPVzoeJbfna5U0WRm6nT+p3yGxXE56buZgbz+3gRqBI48nvUf3YrFHyR0EcVnv5ZH1GlcUZ/yzwBzoB46GnOZxBugA6ZigJTCxEGZ+EVNwwGy3Li3TIi3S4XDpFRcXpiu12Es1mtH99752wEj/ZabR+4fiFtGXBH5vJLCrk/6a/gNHugo9fe4wGOyZDSVJzd/VGqVAR6htV7tppeQkcvrCd56b/D4Ag71bIZDL2nduIp6svGfmJtA5sfK2Wyrio67dl+Oyzz/LSSy9hNBpxd3fnnXfe4aabbip9vkOHDvz888+l3//9vfba5wsLC1m+fDm33XYb27dvp2/fvvUae03VOBkmJCQwZMgQvL29ef/99+nWrRsWi4UtW7bw8MMPc+7cOeDqwKter2flypXcf//9hIWFceONZfd1O3/+PJ6enhgMBn755Rceeugh2rZty5gxY0qPueGGG1i8eDFQkohfeuklJk2aRGJiYplrLV68mBtuuAGLxcLx48eZNWsWWq2Wf//73zX+wVyhaISfYKpjrS4fX4WCEdd8anOEL3JzGKrV0lmj4YzRiPWaef0WSaKJzLkAwK5UYex3I7mtB5Fm9KUw3wLZUFXrryIKFxvuHr+TcvZkmcdDQ9qjSL+aTOWUJMO4OFe6dpMRrN/MVO/bWKvzLpcQdRYlt8aNY4RfX/7j8R3e6fvK3dfdWMCUs78xBdC5+fBbZB82q2Uc1MVjq2Cz4SuOqdI5FpAOAUBXUCCjr6k1A/L9aJehIOCyDsWFxJKNmRvQIb2eD7MyOX5NTeD3Y09B7Clu9vTkrZBQ9Gmp6CwW2n7/KFCy9GdRbi6XzWbcXVT4ubqjlsvprT2DRePNH0kX+O/Gr8gtyMXFRc09Y55CrXIFCWw2Cwq5kg1/LsUu2bh9yL/w1gY06GuuC5Wmfnthnn76aWJiYnB3dycoKKjc3AOVSkV0dHTl8f3t+V69erFu3To+/vhjli1bVul5zlDjn+Q///lPZDIZBw8eRKvVlj7epUsXZs+eXfr9lYFXKPl08d5777F169ZyyTAwMBBvb28AHn30Uf773/9y5MiRMslQrVaXGYR97rnnGDZsGFlZWQQEXP2H6+3tXXpcREQEU6dO5ciRIzV9iWXIm+AYmF2SWKvTcbOXF8q//eN9Li2VQKWSJwICgZJJNhf+KjlnkSDDauWs0YibXE5rVdlpFvEmE5sKClgdWfKJu41KhVwmY3V+Pv5KJZfMZrppGncharunH0UDppId0IPUPDVmgw3S4Xrjf1VRuVpwUWwk48KFcs/1aXPjX92rJWR/tQzz8uxo1O0wmmKZVPQq25WfkG+1l0uIADtzfOiZ8wivRI3hvoKvUBSnVxiHlz6PW85s4xYgV+vP1qjebFbaOFJwocquPxsSB9QpHAhKKdnuozsoJTkDTVH0z/MlOl2G3+U85BeSSsrU1RO93c5ANy1zfP14NDWF/4aGlemiB3grJLT0/4/o9TyflsazgYGM0rqTYbXyekY6bVQqvDcswC5JvHshnvt9/RgSFszjqSn4HPiEkQErkALCeD0xgdndenPHoEmYXX0wqzwwyy0YJRUmiwKDEYzFjXMtp0wuq/eWob+//3WTXW0oFAoMhsa3G1CNkmFubi6bN2/mzTffLJMIr7iS1K5lt9tZu3YteXl5qFSVz2GTJIktW7aQmJjIgAEDKj2uqKiIZcuWER0djZ9f5VUpYmNj+f3334mJibnua6qKwqvpJcM/9HrSrFZu8fIu91yaxVJmaVKW1cL0ywml3y/Oy2VxXi79XF1Z2qp16eOSJPFaRjrPBgbhJi+5gkYu563gEP6dkY5ZkngpMIggl8Y3xmpp1YGCXjeR6RZNRibYLdJfCaryllN1uXkasRnWkpNafh2gm5sn2hw3JMq3DAFMpg5ALBprMrO8DzFf1xugwoQIMO9SJ75Qv8vCVr/SNWU5sgq6RK/wLc7mjlO/cgeQ5RnMr617sllu4njBRaRqlrexyuzs0SSxJySppJROL1BLCoYYoumb503bdAmfS7lwKQkqKPRQG8Pd3Rleg96MY0YDYS4u3ONTMp0/XKXidm9vFubmApBns5FnszHT2xu1XM4od3cumk3IrWaOXjzN2YxMXlXaUWz6otJ7SHIF9r/Wclp9grF4BJQUQVB7Yr5mLaehgddyql0d0yrU6XQcO3aszGPXe2+tCavVSnp6yYe3K92kZ86c4dlnn3XI9R2pRj/N+Ph4JEmiY8eOVR57pa/ZZDJhtVrx9fUtM2Z4RXh4OFAy08hutzNv3jyGDx9e5pj169eXDsIWFxcTEhLC+vXrkcvLDh7PnDkThUKB1WrFZDIxadIknn/++Zq8xHLkGg0yjaZJbeU0RKvlTIeKf0fXJjiAMBdVpcdeSyaTsexv5wKMdHdnpLtjPzk6gqn7CPI6jCKdEHKzrKADdI59k/L0K6YoawV6XV6Fz/fvejNSVtkW2ZWWIUBSkg/hf60G6J3/Dl21Kzj1VxGUyhJipsmFyXE3cWPAAN5z/QaPzENVxhlQkM5dJzdzF5DmE8GWiC5slhk4XXCpZi8YMMls/O6WwO9uQBjQB9wlFUP1bemd60lUmg2vS9mQkAz2+p+I0lPjyseWLHYWFTFcqyXHZuPXwkKG//Vh3VehIEChZK++mEFuWg7rDUz18sQiSczLyOCN4BAUVSw7ktltKDITUWQmVrkkRZLJsPuGYAuMqPe1nK4ejvnguWPHDnr1Kjvjds6cOQ659unTpwkJCQFKFu63bduWzz//nHvvvdch13ckmSRVvwPgwIEDDBw4kDVr1jBt2rRKj4uMjOTuu+8mJiaGtLQ0nn76af75z3+W+QFcmUBz5MgRPDw8MJlMHDx4kEceeYQPPviAhx56CCiZQJOSksLnn38OQF5eHp999hkbN27k4MGDtG5d8gYtk8n4/PPPGTt2LDabjfj4eJ544gl69erFjz/+WKsfzhVxo0djreCTv9B4SCoN+gGTyY3oT2qRJ8UFjmmpVMYnKI+cy8sxGyop4SWTMaPni0j5ZbtfL7cpYmvqgStRM3rMZiyWbABStZN4Rj+rTLutl1FeLiFevYXEO1EnuS3va+SG7Bq/hiS/SLaEd2azVMj5wqoX7deEl13DCH0EPXPcaZ1qxfNiJlJSKjXpb+x8/lyF3aR/t7mwgJfS0jFLdqzAKK07H4eF4fJXkjus1/NuViZ5NhvDtVqeCwzi65wc8u02bvXy5rX0dPJsNu7y8eEun4ZdX1iXtZwh0V7c8lSfBo23OatRy7Bdu3bIZLLSSTLXc6WvOTo6mpUrV9KtWzf69u1L586dyxwXFRVV2r3apUsXDhw4wJtvvlmaDAG0Wm2Zfuuvv/4aLy8vvvrqK954443Sx4ODg0uP69ChA4WFhcycOZM33nijTv3eSh9fkQwbIbtvMIUDppLl05W0HCUWU/WWP9RVQFgGqedXYLNUPs7Ys/O4cokQyrYMQYZM1g3YDkBo8XqmeN/CT7qr60IrayECSJKMZy925xPN+yyK2Ei75NXIarAsICIngbk5CcwFLgZGsyW0A5tteVwsSq72NSqjkxv52T2On92B1sAg8Ld7Mrw4jJ7Z7kSkmHC/kIGUWvH4Z3XFm0y8nZHJQ/5+DHXTkmWz8kFmFq9npPNGcEmLpI+bGytaR5aek2A281OBjtWRUdybeJl7fHwZptUyNeESfV1d6dCA4951Wcupde8IiGToKDVKhr6+vkyYMIEFCxbw6KOPlhs3zM/Pr3DcMCIigjvuuIPnn3+en3766br3qM7gqkwmQy6XV3mc4q96mXUdrFX4ii13GgtLm+7oetxIhjqSzEx7ufJn9S0wIoGkk+uQqkg67Tz7gL6C1pxUtksuKzOEa4d2Jxe9xg7lf9Bdk/iulxABko1qxsdNY3rQYP7tsgi37JPljqlKm8x4HsqM5yEgNqgjm0PassWSTaLecR8Cs+XFrPGIZY0HEAUMhRCbD8MLw+ie40Z4igm3+FSkjKxqX/Or3Bx6uboyx7dkjKsD4Bok556kRP7PP4CACta8vpaezjOBgUiSxFmTiQkeHrjK5fR1deNPg75Bk2FN/H0tp0/gncDNzgypWanxCOyCBQsYMmQI/fv3Z968eXTv3h2r1crWrVv5/PPPOXv2bIXn/d///R9du3bl0KFDZdaXZGZmYjQaS7tJv/32W2699dYy55pMptJB2Ly8PD799FOKioqYPHlymePy8/NJT0/HbrcTFxfHvHnzaN++PZ06darpyyxDGdh0plo3N5JcganXGHKjh5NuCSA/1wp50FDJ71qBEadJPLGlyuPCwjohz6g4vr8vkY6NVdJ/gApJKpmh6WpNZJb3UT7W9ShzXFUJEWB1RhDrZM8yv+0RJmctRGbSVf2iKtA+4xztM87xKHAmtAubgyLZYsog1VC+dm1dpSkKWe59juXeQFtgOERa/RlWGAKPn8PaoTUyE0g5uRWeb7Tby435Xfm+oolCq/Pz8VLIGe3ugc5WMoHqyvIgKxJNqY65eF9yrBonwzZt2nDkyBHefPNNnnzySdLS0ggICKBPnz6l43oV6dy5M+PHj+eVV15h48aNpY9fKcujVCqJiIjgH//4B6+99lqZczdv3lw6COvh4UHHjh1ZuXIlI0eOLHPcrFmzgJKWY3BwMMOHD+ett95CWceKKKq/JvkIDcPu6o5+4FRyQvqQWqDFUHSl/Fn9dn9WSibhH/wniSf2VOvwPpETyiynKHupsunQaJRQq7thNB4ufayv7i06ua7grKHsO3N1EqJNkvNofF8+0Xbhq/CfiEz+ucLjqqtz6mk6p57mCeB4RA82+0fwqzGVTGPNxyirw2a0cTYjjbOUtEjfC77Igo5a2qn9GecSyYGVp9FnFPB+YAhSvo6R7u68mp7Oj3l5DNFqybJaeScrk24aTbnKSzlWK1/k5vBdq5J6ul4KBW1UKr7569z9xXr+4euYWZQNQfnX8ijBMWo0gaal0v3yC6lPP+PsMJo1W2ArCvpPIcuzE+lZcqxOKvz8dwqlDU/vnaTFHavW8VqtD5PDH0SqpP5mWisjGzL3lnls4CADLi6ryjyWqp3MM/qYChdBXG9Szd/dG5rKi3yFOvd8teKvDgkZR1r1ZpN/MFv1yeSaKp5NWxtFZ4tIeDeh3OPeQ7wJvz+c5K+SMWebafN8G7qZAxlUEMj5TRfY/Oc5UoqK8ZDJGeDmxpMBAeWW+TyVmkIvV7cyk2ROGAy8kJ5GjtXKPT6+/NPf32Gvpb5FfPUV7sOGVn2gUC0iGVaD/shRLt95p7PDaHbMHfuR32U8GYoIsrNsjW5hs0pjRa3aTNbl2GqfM3pADAGZQZU+nx5hYn1W2RZmQICMjp2+KXfsD15LWF9Q8UzKmiREtdzOgrYHGJOxCJnZsSXXbDIFf0b2YbNPAL8VJ5Jvrl3XbNb6LAoOF2BKMyFzkeEW7Ubw7cGoQ65fSlF3UEfGmgws2RZUQSqGTuvEra060SFDSUBiAd/sPcTCjJIhljm+vsy6puV33GDg3xnp/Ng6slxxiqag7eZNqCIja33+tXsZZmVl8corr7BhwwYyMjLw8fGhR48evPLKKwwZMqT0nKNHj/LWW2+xa9cudDodERERjBw5kqeffpr27dvXOIaRI0fSs2dPPv7449LH/vWvf7Ft27YKh9wSExOJiopi7dq1TJkyBZlMxtq1a7n55psrfW3V1TBVXps4l3DH1vdsqSSFEsOASaTMeJtD075kT3AMp3JCycpsfInQ1cOE3L6mRolQJpMTaLp+l7q8gteZlSWhVpevozlV/zqeior/RI9q7LQdEoZKWfWfsMkuZ27cIKZIH5MadkOVx9eEQrIx8NJBXjuyge1xZ/lcHsZUn254uNSsDGDxuWJ8R/vS5uU2RD4diWSTSPggAbup8mSvj9OT9EUSPsN9aDuvLZ69Pfn98+N8aP6TR7sdZVr303yQncrAf/Vm1qyR/Cc3l3PhQcg0GqySxOsZ6bwaFNwkEyEKBS5hjntfmj59OkePHmXp0qXExsby888/M3LkSHJyrm4Jtn79egYOHIjJZOK7777j7NmzLFu2DC8vL15++WWHxTJnzhzOnTvHvn3lyw8uWbKEwMBAJk6c6LD7XdH0txdoAC6BgU1u4X1jYXf3pnjgzWQH9iQ1X4NJX/fyZ/XN3VePIXcVxXk1Gxfr2WU8ku76r0tmr/iN12rpBJRdBO9mucR93if4RNe1wnOqM4Z4rZOFWgYX3stDESN4wvwVLrqLVZ5TE0q7tXQvRrNCzd42/djsUbIXo95ayXrMv0Q+FVnm+/C54Zx79ByGBAPaDuWrXQFkb83Go5sHARNLJpJopmsoOl1EzrYcwmLCMKWZ0IRrSOxpIBEDit9V/F+HiwTc7YvLOokI/0hC+/ZDSshFdjEJrrNUprFxCQ5G5qBqT/n5+ezevZsdO3aU7kDfunVr+vfvX3qMXq9n1qxZTJw4kbVr15Y+HhUVxYABA8jPz3dILAA9e/akd+/eLFq0qHTfRCipgrVkyRLuu+++Os8DqYhIhtXkEhaGuYLak0J51vB26HpPIkvbjowssFkdV/6svnkH6shLXo6puKjG57bz6A3F13+NFbUMAVJS/PhrWVwZA3T/5lfXFZw3VHxiTRMiwOdJrflG+Tr/i9rDkLRvkFkdXydSZTMxKm4Poyi7F+OugniMNlOV59v+2oFCoa289qYh3oDfhLITXty7uVN4pGR7KnW4GnOGGXOOGSQwpZtQh6spzNJzef9l2r7Wlodcj0Fv0EhKhhpa0zfPhzZpdrwv5ZRU0XFQmTlHc2lV+UbGNeXu7o67uzvr1q1j4MCBFe7ys2XLFrKzs3nmmYrnTlS0pK4u5syZw3PPPcd//vOf0iV8O3bs4NKlS2VqYDuSSIbV5BIukuH1mLoNI7/jaNIJJSfLCgVAQSPr+6yCX2gWGXHLsdaiEHWr8K7I0qtO9jIqbhnGx0uER3hhtZYdc5NJVmbLvuVZ7q70mkc1dnoPCSV+b2q1E2KxVcHdcSPo792LBYHLCUjdXq3zaqPMXoxqd3ZE9WOzm5q9f9uL8QrJLpH+fTpu7dzQhFe+5s+qs6L0KvsWpvRUYvmrda4J1RA0PYiE9xMACL41GE2ohkvvXSL49mCKThWRuS4TmUJGyF0hbOtgZZtbQkmZub7gYVcz1NCGPrleRKba8LyYBYkpDVJmrirqqPLd6rWlVCpZsmQJ999/P1988QW9e/dmxIgRzJgxo3SbvLi4OIBqleJ0hDvvvJMnn3ySlStXltaXXrx4MUOHDi03NnmlDOe1TCZTma2mqkMkw2pStW5Nw+z21jTYVWqM/W4ip9VA0vReFOmskAVOW/5QR4ERSSSdWo1Uyze6XpHjIKXq42SVXF6SZCgUPbBad5V7Lrx4LRO9bmZjQeXjcEc0Uo0TIsDBfE/65d/PU61G8aDhS5SFda8+cz1upiImntvORKBQ48X2qL5s0ijYXxBfuhdj2rdpGJONtHmxTZ3v5zvaF9/RV4tm5O3JQ66R4xbtRuxzsbR9tS2WPAtJnyfR/v32yF2ujsEWyk1s0l5kkxaIAAaAj11bUkUn15PWKWY8LmYiJafVqMycI6ii6v6zudb06dO56aab2L17N/v372fTpk289957fP3118TExNDQ8yy9vb255ZZbWLRoETExMRQUFLB69WoWLFhQ7tj58+czduzYMo89++yz2Gw164kSybCaNB2azoaf9cXmE0hR/5vJ9utKWq4Ks9FGyXKwppkArwiMOE/iiQ21Pt/d3QdNpobqFAKobMwQICcnjMo2bLhZ/292Kd6j6DqFnWubEAE+SIxmoctbfBW1gz4py8psJlxfPIy6MnsxbovqwytLjlF4vJCo56Nw8b3+mJjSS4lVV/bfnrXAiotXxedZC61k/pRJm+fboL+oRx2sLv2SbBLmdDOaiOtXn8mTG/jJI56fPCgpMzcYAu2ejCgMp3uuGxHJFrQX0pDSMmrwk6g5VRvHtQyv0Gg0jBs3jnHjxvHyyy8zd+5cXn31VWJiYkpbY+fOnWPQoEEOv3dF5syZw5gxY4iPj2f79u0oFApuu+22csddW4bzCg8PjxqPY4pkWE3qBuoeaGwskV3Q9ZxIpqYNmZkSdlPTGf+rmkRA2BEST+ys01UGdJsG6dVLPrLrfMCOPe9Cn75KJKn8hwutJZ77vM+wQHf9akp1SYh5FiW3xo5lhF+fSjcTri+exblsf/8X8s9Z2fNQOJciu1e5F6NrtCvFZ4rxn3B1bWDR6SJc21Zc5TPt+zT8x/vj4uuC4ZIB6ZoPFpJNQqpl+ZlMeTErvc6z0ouSMnPDINzqx/CiULpmuxKebMQ1PhUpy3GFCtRt2zrsWpXp3Llz6dKE8ePH4+/vz3vvvVdmAs0VlZXirItRo0YRFRXF4sWL2b59OzNmzKhw60BHEcmwmtTt2oGLS5OacVYbkkyGqedo8tqPIN0aRF6OFfLBGeXP6pNcYcfbbw9Jp6reAum615ErCNCHIFWzdSy/zo+xuFhCre6M0XiiwucH6ubxq+uPxFUymeaKuiREuLqZ8KuRY7i3sPLNhB3p4Y1Gvj9p4acZbrSy5dFq/2ZGAKaAEHZH92Kz3MSGD3ai9FESfFvJBt7+4/y5+M5Fsjdl49HDg/wD+RgvGQmLKb/koOhUEeYMM+H3lyx9cY1yxZRmovBEIZZcCzK5rMo1jTWRrNTxvbcOvIFoYCS0tQYytDCYLlkaQpL0aOJTkHJrXrBA4e2Ny1+bmDtCTk4Ot912G7Nnz6Z79+54eHhw6NAh3nvvPaZOnQqUbJbw9ddfc9tttzFlyhQeffRRoqOjyc7OZsWKFSQmJtZ6d6CsrKxy+ymGhIQQFBTE7Nmz+eijj8jLy2P+/Pl1fanXJZJhNclVKtRRUZhiq7/urKmwa7ToB0wmJ7QfaUXu6AudXP6snrmobbhqfiX1fMV1dGuid9cbkGqwXdTfC3X/XXFxWxSKipOhXDIzW/49zzOzyvvUNSECvJ7Qic+vbCac/CMyqf56Az4/VPIhc+TSskswFk9NIsaQwd3A4AwrcpU34Z5RnC64hFs7NyL+EUHGmgwyVmegClLR6tFW5Sbd2M12UpelEvFQBDJ5yc/fxdeFkLtDSPk6BZmLjPC54chV9bvs+oIylws+ueADtAfGQCdLMEMKgumUqSI4qQhVfDKSruC619F0rlut5b9zd3dnwIABzJ8/nwsXLmCxWIiIiOD+++/nhRdeKD1u6tSp7Nu3j7fffps777yTgoICIiIiGD16dJndg64nMjKSmJiYMiU3v//+e77//vsyx/373//mpZdeIiYmhldffZUuXbpcd9N3RxAVaGog9dln0f1Ut1qPjYUtIJzCflPI8u5MWrYCayXlw5objdaMzP4LeamO2b/v9oEvIMuofpIo9rLxg2lHpc+HhMiIble+Gs21vvX6hs0F1esu6m2U1SkhXnFjQHa1NxNuCEl+kWwO78RmeyGxRYnODsehupuDGKwLpGOmksDEQlzik5CKrk7f85s7h8CnnnJihLWj1+vx8/Nj06ZN5epKNwYiGdZAzuIlZL77rrPDqDVzuz7ouo0nw6V1o6z6Ut/cfQwYdasoyqn+FkHX07pVDwYqalbRxeBp4zvzjuseM2bsDszmpEqfL1J14An72xRXc5d0RyXEum4mXF8cvRdjYyOToK85lIE6f9qnK+g49R6Ch49zdlg1tmHDBj777DM2bKj9ZLX6JJJhDRTv309izCxnh1FtkkKJsc948toMJc3sjy63eY93Xo9XQAG61BUYi67fBVUTU4c+iSalZiMNRncby6w7rnvMqFGZWG3X3ypqj/dbfK6r/gxnRyVEgHCNqVabCTeEK3sxbrZkkaSv/7FOZ9h4y0YiPBy36N5RHnzwQZYtW1bhc3fffTdffPFFA0dUMyIZ1oBNpyN24KAGX1NUE3Z3L4oHTCUnqBepOjeMxc1z3K8m/EKzyYxfgcXkuHJ6Hp4BTAyeCzXcXcPkZudb+/UXuHfsKBEQWPGbyhU2mYbXNT9wwVj9+zsyIQJMD8qo9WbCDeF0aFe2BLWut70YncFH7cOuGeXXojYGmZmZFBRU/GHT09OTwMDGveWUSIY1dHHK1EY3icYa0oaCvpPJ8mhPeqaspPyZAEBAeAopZ1Zhr+EC3KqMHTQXv/Sa731n1kh8w+/XPUahgGHD12CzXb/Mw2X3mbxQfOt1j/k7RydEhczOx22PMinr62ptJvz5n2Y+P2QmIb/k/l0CFbwyXMWN7SpeG7jkmJlZP5X9EKNWgPElz9LvP9hn4r29Jesinx2i4snBV2eFHki28s+NRr58bTDbglrxqzGFTGMOTdXw8OEsGFN+4blQd2I2aQ259evXKJKhqfNg8ruMIUMWTnaWFQop+apwB7yWKTAinsSTvzi8JS+XK/AvDqr2coprySSopCJbKZsNlMoe2GzXX+fXuugHxnndxNaCitfVVcQRs0yvZZPk/Cu+D//Vdubr8J9oXcVmwuGeMt4Zq6adrxwJWHrMwtQfDRz9h5wugRXXIfVUw/lHrlYjuPbHdyLDxivbTay/0w1Jgkk/6BnfVkm3IAVWu8SDG4x8OcmVPqkn6JN6gqdlco606sVmP8fvxdgQuvl3c3YIzZZIhjXk1q8fed991+D3tStVGPtPJLf1IFL1PhTpLE26/Fl9kpAIDDtO4onrt8Bqq2+3m2q0nOJacjtQee3pUvl5Ebi6VX3cdMOb7JW/ib4GC8YdnRAB4opdGRE/g3tDh193M+HJHcq2AN8co+DzQ2b2J9sqTYYyINi94mUP57LtdA9SMDqq5K2se5Ccc9l2ugUpeH+vmeGtlPQLu3pduWSn7+XD9L0Mz8sUHIzswxafALYVX0Zndtx4cn3pEdDD2SE0WyIZ1pBbv74Ndi+7lz9FA6aS7d+d1Dw1ZsOV8mctdyJMVWRyO34Bf5B06kC93SNK0w1qmQypZjKMi9PQvYecqoodeJjPco93HP/TRV/3uL+rj4QI8E1qKMvlL1drM2GbXWLlGSvFFhgUUfkPpcgMrT8uxC5B7xAFb41WlybOboFyYnNsJOrsSBLE5tjpGijnQq6dxccsHH6g8iUoCsnGoEsHGXQJXpS7sD+qL5u9fNledIlCS813LalvKrmKXoG9nB1GsyXGDGvhwsSbMF907F5wV1hbdUTXexKZmrZkZEnYqzl9XgCliw2t+29kXDxVb/doE9mHfrKxVR9YCUkmsVBdvRbruPGHMBqrLgxgk7nyquZ7LtVgMs0Vjh5DvFY3j2L+F7ia0JTNZR4/mWFj0MJijFZwV8H3012ZWMmY4R9JVuJyS1p/OqPEB3+Y2XXZyul/uhPuWdJa/OKQmfn7S8YMHx+o4sG+KsZ+U8wj/VVY7fDaDhMuCvjPDRqGt6768/+VvRg3eXiysxp7MTaUfsH9WDRhkbPDaLbETve14Navn8OuJclkGHuMJP321zl26//Y1eZfHM+PIi3dLhJhDajdzKhcfq7XRAjQPWxknc6vqgLNtYyG9lUfBCgkA3OUq2sVzxGNRPSQUFRKx78VnCzUMvjCvbwb8BYWr6u7LHTwl3PsQXcOzNXyUF8V960zciar4glOgyKU3NtDRc9gBSMilay53ZUANxn/O3S1kPiDfVWcf8Sd84+482BfFUuPmfFQyxgUrmDuzwbW3uHKR+M1zFhlwFSNyWVX9mJ878hGdiZc5kNVJON8uqBROK5cW230D+5f9UHXERMTw8033wyUlEB76KGHaNWqFWq1muDgYCZMmMDevXvLnHP06FFuu+02goKC0Gg0tGvXjvvvv5/YCuZNTJgwAYVCwZ9//nndezdWIhnWQl2ToaR2pWjY7STe+T4HJ3/BPp/bOJPpT262GP+rDa2XAcm8ityUS1UfXAdeXkGoM+u+u7hcXr0/u8uXvap9zajCZYzxrN3SkfpMiACfJ0XSM+d19kb8A0npikohI9pXTp9QBW+P1dAjSM5/9ldvlwwXhYxeIQri8ypuyWbr7by+08QnN2o4kGKjvZ+cdn4KRkUpsdhLulFrQmMxMP78Lj46somdSWm8q27LKJ/OqOSqGl3HEQaGDHTYtaZPn87Ro0dZunQpsbGx/Pzzz4wcOZKcnKszbdevX8/AgQMxmUx89913nD17lmXLluHl5cXLL79c5nqJiYns27ePRx55hEWLmmbrVYwZ1oJb/5onQ5tfCEX9p5Ll04W0HCUWk/2v3R+clwBf+e5OcovKbzUzrPMU7hj2fxWec+TCTjYcWkxOYToBXuHcPOB+urS6WjNw2/EVbDu2HIBxPe9gTI/bS59LyDjL8j3/4alpC1DIqzFwVg2e/oUUZqzEUJDvkOtdz4DOUyG97q11uVyOvRr7JiYmSkS3C8Jiqd52QLca32avfB7GWuy+UF9jiFcUWxXcVclmwnYJTNVc+WKzS5zMsDOxXcVvXY9vMfH4QDXhnnL+TLGVWQZqtUvUpbPl73sx/h7Vl81/24uxvni4eNDVv6tDrpWfn8/u3bvZsWMHI0aMAKB169b073+15anX65k1axYTJ04ss0tFVFQUAwYMKLc90uLFi5k0aRIPPfQQAwcO5KOPPsLVtfqznBsDkQxrwSUwEHXnTpjOXH88x9y2OwU9JpLhEklmlg3JABigsewA8fQtnyFdU0EkNfcSn254hl5tR1R4/MX00yz57Q2m9J9L19YDORT/O19ueYVnp39BqG8UKTkX2HBoCQ/e8CZIEl9sfpGO4X0J82uDzW7jx90fM3P44w5LhL4huWRdXI7FaHDI9a5HoVDiWxRQq+UUf1fdlmGJbvxVNb1KnqZT3O19ga91tdv4tb4TIsCWn9bQrc0gHurYnSm671ixP5kdCTa23F3SBXnvWgNhHjLeHltSbHveThMDwxVE+8rJN0q8v8/EZZ2dub3Lt9C3XrASm2Nj6c0l5/YLU3Au286mOAtJBRIKmYwOfo5p/XoYdUw9+xtTuboX42aVjD918djqoaD5wNCBKOWOebt2d3fH3d2ddevWMXDgQNTq8t2/W7ZsITs7m2eeeabCa1y7XZMkSSxevJgFCxbQsWNHoqOjWbVqFffcc49D4m0oIhnWkvuIEeWSoSRXYOw1hrzo4aRZAtDlWiEXGuvefx6u3mW+//XoD/h7htIupOLp2ztOrqFTRD/G9rwDgEn9ZnEu+TA7T61j5vDHychPIsy3DR3CSma8hfq1KXnMrw3bji8nOqQbrQMdsy9kQHgaqWdXYrM2TMu6b7dJSDrH3KsmyTAzIwgf36qPu2K47lW2qr/jsql2yay+E6KtWEf2+o94bXkub6i19G4dxOZ7dYz7a6/aRJ0duezqzyfPIHH/LwbSiyR8NDL6hCrYN1tL54CyH6gMFolHNhlZfqsrclnJuGy4p5xPbtQw6ycjaiUsvVmDq0v1x2yry0ufx/TT25gO5LgHsDWyF5uVNo5eZy/GmhoSOsQh1wFQKpUsWbKE+++/ny+++ILevXszYsQIZsyYQffu3QGIi4sDoGM19nHdtm0ber2eCRMmACWl1xYuXCiSYUvhMXIkOZ9/gV3rSfGAKeQE9yZNp8VQ3DS3P7LaLPwZv43R3W5FJqv4DeNS5hlGdytb8aRTeF9OJJQMuof6RpGpSya3sKQlk5mfTKhvJFm6VPaf38yztzimNmFgxEUST/7UoGXxIlVdcNTv9No3+6rExsoZNFiD3V698UCFpGe2y0+8appc2/BKE2LcnhQsDp7E5T+xbPd7BvCdXx79/9pMeEdM2aUQ82/QMP+G6+8+D+DqIiuzMP+Kub1VzO3dcON7fkVZzDj1KzOATK8Qfm3Vg81yEycKLiLVoSDGkDDHJUMoGTO86aab2L17N/v372fTpk289957fP3118TExFCTRQaLFi3ijjvuQKksSSczZ87k6aef5sKFC7RtgE2IHUVMoKklTbduJM5awJ7B7/KnfRAXU9UlibCJOpGwF4OpiAEdJlR6TIE+Fw83nzKPebj5UGDIBSDYpzWT+8/h0w3P8OmGZ5gyYC7BPq35cfd8bh7wAGeT/+TNFXN4Z9U/iE+teM++qgSEnyTxxLoGTYTRUf0gy3G/25q0DM1mULnUrOpIdOESRnpUb0JKZY5oJNoNDcNF4fiW1N/tzPGhZ8IjLA5+GZs2qN7v11ACdWncfXIzy45vZ0u+nSc8OtPZI7LG12nv055greM2871Co9Ewbtw4Xn75Zfbt21e6dyBA+/YlM5nPnTt33Wvk5uaydu1aPvvsM5RKJUqlkrCwMKxWa5ObSCOSYS3J5HJUYaFYa1ioubHad24TnSP64631r9N1hnWezCszlvLKjKUM6zyZ/ee3oHZxJSqoM9/v/JD7J7zOLYMeZNFvb2CxVf8NWyaT8A/+g6STW+sUX210Cx3p0OtV1vKuTEFBZI3vcbvpHTTyuiWyhkyIULKZ8JCidzkVcReSzDHjyo1FSF4Ss05sZvmJXWwoVPCoZ1fau7eq1rnjWjfMdk2dO3emuLikSML48ePx9/fnvffeq/DYKxNovvvuO8LDwzl+/DjHjh0r/frwww9ZsmQJNgfXBK5PIhnWQZueAc4OwSFyCzM4n3KEwZ0mXvc4TzdfCvVlazkW6vPwdK14UKvIoGPT4W+5bci/SMg8R6BXOIFe4bQP64XdbiUzv3p7zylcbHj5/kby2T+q94IcyNs7BFWGY9+YazaBBuLjq1GX7W+8TMe50yOhxuf9XUMnxHSTiklxN/Gw+3yKAvs0yD0bWqvsS9x/fCOrT+7hp2I1D3l1I0obVunx4yPHO/T+OTk5jB49mmXLlnHixAkuXbrEypUree+995g6dSoAWq2Wr7/+mg0bNjBlyhS2bdtGQkIChw4d4plnnuHBBx8EYOHChdx666107dq1zNecOXPIzs5m8+arBRd0Ol2ZhHns2DGSkirft7OhiWRYB+EdfVBpmv4n2D/Ob8bD1Zsura6/jikqsDPnU46UeexcymEigzpXePzqPz5jVPfp+LgHIEl2bParnxJtdluZmayVUWmsaNTrSY+vXbdqXQ3oPIU6zcevQE3GDAFycuyo1TUrtwYwQvcKEeq6/4k3dEIE2JjlT7ekJ1ge+hx215rvDtJUtMmM45/HNvDzqT9YZdByv1c3Ityudom29WpLG6/azQ6ujLu7OwMGDGD+/PkMHz6crl278vLLL3P//ffz6aeflh43depU9u3bh4uLC3feeScdO3Zk5syZ6HQ63njjDQ4fPszx48eZPn16uXt4eXkxZswYFi5cWPrYjh076NWrV5mv119/3aGvrS5EObY62rroNLEHqzf1vTGyS3Ze/f4u+kaPZuqA+8s8983v7+Cl9WfqgLlAydKKj395nKn959Kl1UAOX9jOr0e/L11aca2zyYdY/+dinrz5E+QyOXlFWcz78V7mjn+NvKIsfvlzIf++60dUysqrerh5mLCZ1qLLSHX8C68GpVLFrR2fRnLwWPDa4KPk5OfW6JzhI/KQpPU1vlec51xeK7yxxudVpLdRVi+TaqrSytXIwvBNRDfCzYTry5W9GIMjR3Fnj/urPkGoM9EyrKMOAxw/sN2QzicfIa8ok4Edbij3XG5RJgX6qxUp2gR3IWb0i+w9u4F3Vj3AsYu7eGDCvHKJ0Gw1sXLPJ8wc9nhpK8jHPYDbhjzCsh3vs+Xod9wz8tnrJkJPvyKMBT84LREC9Os+2eGJEGreTQqQnORT9UEVaFfwNcM8HFPY3RktRIBEg4ZxcdN42utDDA5aeN7YdUk9xRNHN3BnaMVrfgXHEy3DOrLbJb55fi/FurrN3hOu8gnKJzdxOSb99Te3rW939H/RobNIr/gl7CQZOTXbeV0mg5GjNmC11qxFCaBT9+Jxy8uYHPSn7qwWIvy1mXCbI0zKXlitzYSbtOBu8OAeZ0fRYoh1hnUkl8to1z+YY1sTnR1Ks+AflkHa+ZXYLM79cNG+7cB6SYRA6aLwmpAkkMu7AztqfK6X6SgzvS+zRFe92YtVOaKR6D00rExCLDy6kcKjG7HqSoYMXPxb4T14Jq5tK9/yrODPnyg8thFbQRZyV0/cOgzBZ8R9yJQl6wKLTm8nf+dSJLMBbbex+I65v2Qz4Qt9edccQOp3z3FsNniqG7al2mC63lr1MYLDiG5SB+g4sGl3lTYWgRGXSTn9g9MTIUDX4GH1dm1ZDSfQXJGdFVLre47SvUKYynF/7n/vMlV4+OEz4j5C7vuYkPs+RtO6B5lr3sCcdbnC84vP7CBv5xK8h8wkdO7n+N34KPpzu8nbuRQAm15H7uZP8Bk1m8A7/k3xmR3o4w+Wnn/4p0UUDf4XH4Z8gMmng8NeV6Mhk0M3kQwbkkiGDuAX5o5/RPnqF0L1BUacIfHE6mrNMK1vfr7huDh4OcW1atMyBDh/XolMVrtdM5RSIXPUm6s+sAauTYhu0QNwbdsPF98wXHzD8Bl+L3KVBlNqxTvem1LOognvhLbzSJReQbhG9cat03DMaSVlwKz56cjUbmg7DUcd0h5Nq+5Yckqm4Ref2YlMrsStw2CWpobRPeNltoU/gqSqfCPfJqftGPAKd3YULYpIhg7S1CfSOI1Mwj/0TxJPOPaNui76dZzs8OUU16rp0oorjEZQq2s/gaRDwf8Y7O7Yrt+KJtVIdhvFZ3ZitxhRh1Vc21Id1glT+oXSZGnJT8dw4VBpt6rSNwzJYsKccQGboRBzWiyqgEhsxiLydy/Dd9yDpdcy2eXMjR/MFOlj0sIqr6DUpPSJcXYELY6YQOMg+gIzS5/bi70W2+e0VHKlHS+fXaTFHqn64Abi4qJhevsnkfT1V1pva2Qsl9Nrt9h40GA9ylpu5AuQp+nHE+bnMTv4z763UcapNXtJXvokktWMTOVKwOSncG1b+XZnBYd+Jm/7IkACuw33njfiN+Hh0uf1sfvI3/0dktWMtstIvIfeRfbG/6AKiEQV3JbcbV+C3YrXkDvRdhxaet5DEQk8Yf4aF91Fh77GBuMRAo+dAoWY0tGQxE/bQdw8VUT18OfC0Sxnh9IkqDRW1KrNpFWwY7Yz9es+GSm3fmvMyqn9hI+LF7S0r8MQmY/xT2Z4J/ONrvKKJ7VxRCPRc3J/ZMpPMBuK0Z/fQ/aG+QTd+Q4q//ITd4yJJ9DtX4Hv+IdQh3bAmpdK7ravyN/7A95DZgLg1n4wbu0HX3POSSxZCfiO+wepXz6A/+SnUWh9SPvmCTQRXVFovYGSzYS/Ub7Ol1F7GJy2FJm1dpseO02ve0QidALRTepA3UdHODuEJkHjbkYurSHrcuNKhAAR8vb1fo/adpMCZGRIqNWt63T/0QWvEurAyTRXHPNQ0mVKP9zD2uEzIgZVYBSFh36u8Nj83ctw7zIajx4TUAVE4tZ+MN4j7qVg/6oKx40lq4XcXz/Hd8LDWPPSkOw2NK264eIXjotvGKa0smOTVzYTnqH8mGwH15atVzI59L7X2VG0SCIZOlBoO28xkaYK7j56rMU/kp9evbqkDalju6GQXf87j9R2As0VVmvF5e+qy8Wex2xN/RQ8v3YMUZIkJFvFC/4li6lk8eQ1SmfZVtCFq9v3I5o2vVEHR4Nkh2tK+0l2K9grnnh1IN+Tvhcf4NOgeVg9msCElOhx4C0+VDuDSIYO1n1UE/iDcxLvQB2FGcsoys12digV6hLo2D3jKlPbpRVXpKbUvVZnJ91nDHR33I4ChV/9F/Pxw9jSUzmQGos9bjWmxJNoO48EIHv9h+TtXFJ6vGt0fwqPbqT4zM6SyTOXjpK/exmu0f2RycvO5DVnJ1J8bjfeQ+8GQOkbDjI5hcd/RX/hTyw5yahC2l03vg8uR9Mv/y0OR8xCUjTc/oY11neWsyNosUTHtIO17xfMH2svYCh0TAms5sI/NIv0uOVYzc5fQ1gRf/9WKNNlUIcNWKurLmOGAPHxMkLDPLHZCup0nbssH3FY9gwWB0ymsefnonvnZey52ci07hS1aUe/Nz4jT98Ki03CWpBV0gX4F6/BMwAZ+buXYSvKQe7qhWt0f3yGl90dXZIkcjd/is/ouchVJZv8yl3U+E18jNytnyPZLPiOexClR9Vbj+VZlEyPG8dI37587LkM7/SG3wXlunwioZ1jd6gQqk/MJq0H+9dd4PDmihcbt0SBEUkkn16DvRHvbTZxyMN4pDZMF/fe6ETOJsfV6Rpjx13CZKp7qa5N3p+yTFf7xfxVcWbptqq8FnWWewq+RFHcSArt3/g+DHjA2VG0WKKbtB50HRGOvI4bqzYXgRHnSTyxslEnQpWLK555Xg12P7kD/uxycx3THT+24BWC62EyzRXOKu5dHa9durKZ8J3O30zY1Rd63e3cGFo4kQzrgbuPmui+gc4Ow8kkAsKOkHhig7MDqVL/HlORDA2XrOvaTQoQe16FjLq/gbvYc5mj/r3O17mexpwQSzYTnuT8zYT7zQVVzTdxFhxHJMN60ndi5N8ny7UYcoUd38DdJJ3a4exQqiWcmm+cWxd1nU0KUFQkodZ0ckA00LngE/o7cDJNRRpzQoSrmwmvCH224TcTdnGDAQ9WfZxQr0QyrCc+wVqi+7S81qGLyoq7xxZSzx9ydijV0rnDcKSchp3sJHNAyxBAr3dcEr/b+h+U9ZynGntClCQZz1zswUjj+8RF3IpUx1m/1dYnBrS1T8AxMTHIZDJkMhkuLi5ERUXxzDPPYDQa6datGw8+WHGi/fbbb1Gr1WRnZ7Njxw5kMhn5+fnljouMjOTjjz+udXxNhUiG9ajvxKgW1TrUaM0oFT+Reemss0Opts7+gxr8no5KhgkJng65DsDe5Zsw3T+NzElDyZw0lNxH7sV0oPIJOpLVQtE3/yP7rslkTBhAztzbMR3cW+YYw7aNZN1xA5lThlP42QfA1YRIYSYpXz6A3aR32GtwlJLNhG9pmM2EFSoY/K86X+aGG24gLS2NixcvMn/+fP73v//x6quvMmfOHH788UcMBkO5cxYvXsyUKVPw9696Jm5LIJJhPfIN1dK2d8toHWq9DdgMK8hLbTqzaAMDo1CkN/x9HTFmCJCSLKFShTrkWgH+Ch6ZoyL66x/w/fw7VL36k//y41gvXajw+KJFn2H4ZTUe/3oGv8WrcZ18K/mvPIkl7hwAdl0eBR/Mw+PBx/F573MM2zZi+mMXUJIQ9QcX4j8qBrm68Y6TrUoPomvKc6wPfwJJXU8TrHreBZ51/x2q1WqCg4OJiIjg5ptvZuzYsWzdupW7774bg8HA6tVl69leunSJHTt2MGfOnDrfu7kQybCe9Z0YiYPe+xotr4AC9NnfUVjJ7u06vZHv9x/llXW/8tzqTXywZRdJufnXveaRyyl8uGUXz6/exOs/b2P5weMUm66uUYxNz+KdjTt4cc0Wvj9wDKvtagUSg9nCOxt3kFt8/VZHv3Y3gRN2jHJUyxBAsndxyHUGDdYytL+Vf7a/jDKiNe5zHkHm6obl7IkKjzduXY/2rjmoBw5DGRqO29TbUQ8Ygn7ltwDYUlOQa93RjJqAS8cuqHr2w3r5EgCG3zZRoFLSa+7tjbbL9AqbJOeR+L6Mt37E5fApjr24ixuMfM6x1wROnTrFvn37UKlU+Pv7M3XqVBYtWlTmmCVLlhAeHs748WJd4xUiGdYzvzB32vYKcHYY9cYvJIfcy8swFFa8AFxvtvDp7/uQy+XMHdafpyeMYHKPTriqKt+X71J2Lj8cPEb/qAiemjCCewf1JjE3n5WHSt6Y7ZLEdweOMahtK/41ZjDJufnsv5hYev7Gk+cY1LYVvtrKWx1qjRaPXMd1M9aEo1qGAOnpju156KabT2+NBePvm5GMBlw6d6/wOMliQaYqW8lFptZgPnkUAEV4KySTEUvcOewFOqznT6Ns2w57YQHFiz/H49HnGv0Y4rXiil0ZET+DV33fd9xmwgMeBA/HbP22fv163N3d0Wg0dOvWjczMTJ5++mkA5syZw44dO7h0qeTDiCRJLF26lPvuuw+5vGwKCA8Px93dvcxXYmJiufs1R6ICTQPod1MUF49mVVRysUkLiEgh5fRq7LbK63luP3cBbzcNM/r3KH3Mz/36XWOXs/PwdXNjWPuo0uMHtW3F9nMlW/IUm8wUm8wMjm6Ni0JB59AgMguKAEjIziUpV8e0Xtcf6xnQbSpStnPWPjpineEVsbFyhgS6YbfXfezt4kUzj/4rBbNlAJJGi/frH6KMbFvhseq+gyheuQyX7r1RhEZgPnIQ4+7fS2uGyj088Xx2HgXvvIxkMqEZNwl1v8Ho3n8N15vvwJaWQv5Lj/Gr1Uq3Ox+kSNG5US7M/7ulqWH8KH+Zz9oeYHTGImTm4tpdyNUHhj7msLhGjRrF559/TnFxMfPnz0epVDJ9+nQAxo0bR3h4OIsXL2bevHn89ttvJCYmMmtW+dJvu3fvxsPDo8xjI0eOdFicjZloGTYAvzB3OgxsXpv/BkbEk3RyxXUTIcDp1AzCfbz5Zt9hXv1pKx/9upv9F67/SbO1vw/5BgNn0zKRJIlCo4kTyel0DClpYburVXhq1MSmZ2O22riUnUuItwc2u53Vh09xa59uVRY9CLW3qdkLdiBHdpNaraByqbj1VlMRES7878twPv00lN633YTu3VewJlQ8ZujxyNMow1uRE3MLmeP7U/jfd3C9YUqZkmuaYaPxW7gS/2U/4x7zIObjh7BejMNt0i3o3ngOj4efwvu1Dzj+n9dp1dW1SbQQoWQz4Tlxg5jK/NpvJjz0CdA4bhxSq9USHR1Njx49WLRoEQcOHGDhwoUAyOVyYmJiWLp0KXa7ncWLFzNq1CjatCn/NxAVFUV0dHSZL6WyZbSZRDJsIAOmtEVZj5U+GoqEREDYcRJP/Fzh7gJ/l1uk548Ll/F31/LA8P4MbtuadcdO82dC5btWRPn7cueAXiz74wjPrioZM9S4KLmld0lrTyaTcc+g3mw9E8f7W3YS5u1F/6gIfj97gehAP5QKOZ/+to93N+1gT1xCuet37TQKKdd5tWMd2U0KkK8rv19gbbi4yAgLc6F9ezX/jrmMW3QH9Gt+qPBYubcv3v+eT+DGffj/sBG/pWuRubqhCKl4n0TJbKbg47fxfPwlrClJSDYbqh59UbaKRBHeiiMXTzSZLtMrThS4M+jCfbwb8BYWrxp8uPIMh/71V3ZNLpfzwgsv8NJLL5XOIp01axZJSUmsWbOGtWvXiokzFWj6785NhLuPmp5jHfOm5SwyuR3/oH0knfqt2udISIT5eDKxe0fCfLwY2LYVA6Nasf9C5bNO03WF/HT0NGM7t+OxcUO5f3h/8ooNrD58svSYqABfHhs3lBdvGs0tfbqSW6zn8OVkbujagR8OHGNA21b8c9Qgtp6JIzW/7HhmR58BNX/xDuTot/u4WI3Dr6qyZRIiy0OyXL+wukylRhEQCDYrpl2/oR4yssLjipd9hbr/YFzadyrZbuna8nxWK5Ld3qTGEK/1eVIkPXNeZ2/EP5CUmqpPGPkcuFTjuDq47bbbUCgULFiwAChp8Y0ePZoHHngAtVrNLbfcUq/3b4pEMmxAvca3ws2rEW8fcx1KFxue3r+Rcu5Ajc7z0GgI8iw7BhHo6U6evvy6pyt+P3eBSH8fRnVsS6i3Jx2CA7ild1cOXkqmwFDxruWrDp1kco/OSEik5BfQIzwED42atgG+XMzKKT0uODgaRbpzx6bkkoNbhvkSGnXdNiX++utcTpwwkJ5u4eJFM19/ncuFw7F0velGAHRvv0ThV/8tPd5y9iTGXb9hTU3GfOII+c8+ApId7YyYcte2JlzAuONX3GP+CYCyVSTI5Bg2rsW0fzfWxARcOpTMim2qCbHamwkHdIKed9Z7PEqlkkceeYT33nuP4uKScc05c+aQl5fHnXfeiUZTv8m4KWoZncGNhEqjZNC0tvy2pOksSgdQu1lQyjaQceFijc+N8vchq7CozGNZhcX4uLlWeo7Fais35if7q3pBRWnswMVE3FQquoQFoTeXdH/a7HZAgc0uYb/mpL5tJ0JqjV+GQzlyAs0VRlN74HyVx1UmP8/Gu+9kkZtrRauVE9VGzTvvBBPR9whPGQdhy0yHa2YeSmYTRYsXYEtNQebqhnrAEDyf/zdy97IffCRJouCjN/B46ElkriW/c5lag9ezr1Pwn7fBYsHj0WdLWpd/OaKR6D00rNHudnE9B/I96Zv/AM+0HsUD+i9RFqaUPWDieyB3bFHwJUuWVPj4c889x3PPXV26MXPmTGbOnFnhsSNHjqSyDYwSEhLqGmKT0ORahrt27WLy5MmEhoYik8lYt25dhcf98MMPKBQKHn744dLHDh8+jEwmY//+/RWeM2bMmNLug2tLHMlkMvz8/Ljhhhs4caLidVfV1WFAMEFRzpnSXxtaLyOSeRU5yTVPhADD2kdxOSef387Ek11YzJHLKey/mMiQ6MjSYzaeOMcPB46Vft85NJCTyensi79MTpGeS9m5rDt6mghfb7xcy36iLTSa2HY2npt7l7Qs3FQuBHq6szvuEgnZecRnZhPp7wOARuOOe07DbNPU0JISvet0/lNPB/Dd963YtLkNq1ZH8v77IfTp60ag4XemeeXgO/9rvJ6dV3q8qkdf/BevIWjLAQLXbcfr+TdQ+Jdf5iGTyfD972LUg4aXeVw9aDgBP24iYPU23G4q32XXVFuIV7x3uR398t8uu5lwl1sgavj1TxScpsklw+LiYnr06FHaF16ZhQsX8swzz/DDDz9gNJZ0rfXp06d0ttXfJSQksH379jIDy1dKHKWlpfHbb7+hVCqZNGlSneKXyWQMu719kyjT5ulfhCH3ewqy0mp9jVa+3sQM6cPRpFQ+2LKLbWfimNqzM71bX51oUWA0lek27RcVwZSendgbn8AHW3by7b4jBHpoiRlcfleBn46eYUT7NmWS5Ix+PTiWmMqiPX8yokNbWvl6AzCg+81IJudvJeXoblKAhASJFcstjB1zkc8WZF/32J07i5gVk8SNN1xi7twkDhwouyxjxYp8bp2ewK3TE1i5Ip8bC1/FV1nyVmE5e5Kcf9yJVMUsYkdo6gnxymbCs9TzyQ0bDRPedHZIwnU06c19ZTIZa9eu5eabby7z+KVLl+jSpQtpaWlMmDCBRx99lDvvLOmn/+STT3jppZdIS0vDze3qerfXXnuNr776isTERBQKBTExMeTn55dpee7Zs4dhw4aRmZlJQEDdFtLv+jGWkzsqn1HpbL7BuWQnrMBsaHy1I2tFJmNGrxeR8pw3i/SKS22K+C21ZmOvVUlJSWH9+h9xczPTs6eGfz5ccb3J06eNPP5YKnPm+jJwoBu//17E8h/z+fyLcKKiVFy8YOJf/0rljTeDkSR46cV0Pl0Qhq7Xy7yf15vch+7G84mXcenomMo31dGYNwiurhcmduSB4RWv2RQahybXMqyOxYsXc9NNN+Hl5cXdd99dut4G4K677sJkMrFq1arSx65UZIiJiUGhqLg/v6ioiGXLlhEdHY2fX923eBl4cxvcfdR1vk59CAhPJyNuWfNJhED3TmMaRSIEx7cMzWYza9as4YEHpuDucf0/6TVrdPTr58Ydd3jTurWKWbN8iW6n5qd1OgASkyxEtVHRq5crvXu70qaNiqQkMz117+G5Ziku3Xs3aCKEpt9C7BjswewhUc4OQ6hCs0uGdrudJUuWcPfdJbtGz5gxgz179pSWIvL19WXatGllukq3b99OQkJCuYoMV0ocubu74+Hhwc8//8zy5cvLlTCqDZVGyYg7HVTWyYECW10i+dSP2Kz13w3WkDp493N2CKUc/Za+ceNG2rVrh0bTrsoF/WfOGOndp+zkpX59XTlzxgRAVJSKlGQLGRlWMjIsJCdbiIxUkZZqpmj9t3jOfriiy9a7ppoQ5TJ465ZuKBXN7q222Wl2v6GtW7dSXFzMxIkTAfD392fcuHFlkt/s2bPZtWsXFy6UVNdYtGgRI0aMIDq67P5wo0aN4tixYxw7doyDBw8yYcIEbrzxRi5fdszODJHd/GnXt/HsahEYcYrE42uRJCdUr65HoSHtkac3ntfkyJbhqVOnSEtLY+zYsZhMIJNff4JQXq4NH5+yvR/ePgpyc0vGUlu3VjF7ji/PPpPGs8+kM2euL61bq5g/P5uH5mrocfpXsmffSs4DMzAfP+yw11EdTTEh3jmgFb1b+Tg7DKEaml0yXLhwIbm5ubi6uqJUKlEqlWzcuLG0FBGUzBpt1aoVS5YsoaCggDVr1lRYkeFKiaPo6Gj69evH119/TXFxMV999ZXD4h12R3s02sqLVjcEmUzCP2Q/iSd+dWoc9aVPmxsrXpPhJDIHJUOdTsfmzZu55ZZbSktm2ax1Xz82ebInS5ZGsGRpBJMne/LrlkLcXGV07qxm77w3af3mfDweehLdG88hma+/KN/RmlJCbOXrxvM3dnJ2GEI1Nat1hjk5Ofz000/8+OOPdOlydVzDZrMxdOhQfv31V2644QbkcjmzZs1i4cKFhIWFoVKpuPXWW6u8vkwmQy6XV7hRZm25eqgYcms0vy11ztpDhdKGh9cOks8cd8r965ubmyfaHDekv/ZqSivM4u0dX7D94gEMViOR3mF8OPF5eoR0rPD8xze8xapTm8s93t4vkt/mfgPA2tO/8vbO/6E3G7it20ReHfNI6XFJujTuWv4kG+77Cg+1FoBvtyxnwU+LGDBgADfccEOVr+HUqVOsXr2aDh06MGPGjNLHt2/fTnFxMV988cXVdZiSxKlTsG5dAW3auLDgs3AU1yQOH18FeXllZ9Tm59nw9a14rFyns/HNt3nMnx/K2XMmwsOVzO6YwUch/ZCsVqzJl3Fp067K1+BITWEdolwGH93eA626Wb3FNmtN7jdVVFREfHx86feXLl3i2LFj+Pr6smbNGvz8/Lj99ttL3xyumDhxIgsXLix985k1axbz5s3jhRdeYObMmbi6ll8EbjKZSE8v2f01Ly+PTz/9lKKiIiZPnuzQ19RxUAhxhzJJPJ1T9cEO5OJqQa3cTHp8XIPetyEN6DYNKbMkEeYbC7ll2cMMatWLb257Dz83by7lJeOl8aj0/NfHPsrzI/5R+r3NbmP84tnc1HEkALn6fJ7e/B4fTXyeVt6hxKx6liGtezM2ejAAL/76Ec+P+EdpIjyWdpa1ezYQFBRUrfjz8/P59ddfadWqbCm/jIwMTp06xeTJk5EkiU2bNjF9+nT27NlDly42zp7L4bHHA8okQoDOnTUcPWJg+vSrRaIPHzbQuXPFk7k+/yyH6dO9CAhQcv68CatNolf+e3TVruB3m62ktJoTNPaE+I8Rbekb6evsMIQaaHLdpIcOHaJXr1706tULgCeeeIJevXrxyiuvsGjRIqZNm1YuEQJMnz6dn3/+mezskjVYrVq1YuzYseTl5TF79uwK77V582ZCQkIICQlhwIAB/Pnnn6xcubJetjQZc18nXD0brlSbq4cRuW012YnNNxEikxFkjij99vP93xHiGchHNz1Pr9DOtPIOZURUfyJ9Ki4uDeCpdifQ3a/063j6eXTGQm7vVjImfTk/FU+1O1M6jaFnSCcGtepFfE7JmPK6M9tQypXc2GEEAMVmPY/+8m9enPlEtcph2e121qxZw8iRI/HxKTvulJ2dTXBwML1796ZPnz4EBwdjt9txcXEhNVVGv75udOyo4Z13Mvn669zS8265xYs//9SzckU+iYlmli7NJTbWxNSby++gcPiQnuRkC1OnlhSJ6NBBTVKihUMHCmm97nGQK1BGtK7yddSXIxqJ9kMaX5dp5xBPnhhXt/J4QsNrci3D65UNup7bb7+d22+/vcxjW7ZsqfT4JUuWVFrmqD64eaoYe18nfvn0eL2Pb3n4FqPPWUlxfm7VBzdhPTuPQ8q/upxia/xehkf158F1r7A/6RjB7gHc2+tm7uxZ/Zb+8hMbGBrZh3Cvki25onwjMFiMnMqIJcwzmBNp57ij203kGwv5YPdCVsz8uPTcl7bOZ3TbQQzq0Bt+qfpeO3fuRKvV0rt373IbrAYGBpKTk4NOp0OSJHJycggMDMRqtXLpUh5vvxMCQGamlWsr23XpouGFFwNZvCiPRYtyCQtz4fV5wURFlf0gZjLZ+eSTHF56ObC0NF5AgJJHHvHj/fezcHHJZsK8eRxVO7fG5WFXiT5Dwojd2zhaiCqlnI9n9MRFzB5tcppcMmzOWnXxo8foCI7/llRv9/AJyic3cTkmfS03JW1C2nn2Af3VbrzE/DSWHf2Juf1u55FBd3M87Ryv/PYfXBRKbut2Y5XXSy/MZvvFA3wy+eXSx7w1Hnx00ws8tv5NjFYz07tOYGSb/jy18R1iek8jUZfG7NUvkGPIR46MnQ98R6G96pZMYmIiR48e5cEHH6zw+YCAAMaMGcO3334LlEwKCwgIQKVSMXnyVI4c+ZMli5NQKGXccGPZbuARI9wZMeL6s07VajlLlkaUe3ziTZ5MvKmkpWhQ/sqTspvQWZ07U7cxJcSnx3egfVDl3e5C4yWSYSMzaFpbUmLzyE4qqvrgGvIPzSQtdgW2KrblaQ7Cwzojzyj7Jm2X7HQP7sBzI0r2kusa1J7z2ZdYduznaiXDVac246lxZ0L7YWUev7H9cG5sf7Xm5B+JxzibdZF/j3uMoV/O5LXRj/Lirx9islooMumRS9dPRCaTibVr1zJ58uQyVZL+rm/fvvTt27f0+2PHjqFWqwkPj+CD99fy6YJgsrOsvPlGJt8ua4VK5djuRFdrIrO8j/CxrqdDr1sbjSEhDmzjy9xhYnF9UyWSYSOjUMoZP6cLK94+hNWBdTQDIy6TdGotkpMmPDS03lHj4W8bBgS6+9HOP7LMY9F+rdl4fmeV15MkieUnN/D/7d15fFNV2gfw383N1iZdaJuSLqGhdoMuUCplKWDRIsWyiJVahkpbwVEER0ZfQRwdsJQCioII+g5MF1xY6gwgoIIsiiOyI4uKFBhAlpalpTtd0tz3j76NhBS6kOTeJM/38+kHenPuPSdp0qfn3HOe80T4o5Cyd18KU69rwBvfvIclI/+G8zcvo0nfBAkrxs265j0VY5aPBcM076px4cIFHDhwAG+88YZRIoebN2+ivLwca9b8sbFuy62BrKwsTJs2DR4expMzamtrsXv3bmRmZuLy5cvw8fGCv78E/v4S6HQcLl1qRGCg+e9Jx5TnoKeiEL8KIFkRnwHRUyHFkqeiW52vQGwDBUMB6qJWYHBKML795DezXM9bcxK/H//aLNeyBQpFFzhfdzIsp2jxoF8kzpYZD0H/t+wi/F3bntm57+JRnL95GalRSfcst/THj/FQYCwi1aH4+WoRdPomDAqIwfZnCvDcxjcxMXosovr0wrTP3oSXlxfi4uJMMhp5eXlhypQpRsd27dqFhoYGJCYmws3NdLLLtm3b0L9/f7i6uuLy5cuoqeHAMGJwnA7Nkz4tExxEaEIm8jEDmYJYyslHQBQxwPup0VC70R6Btozu8gpUzzhfhA30uc+rcPDyPeRQgRAA+kWMAddg2gOe3HccfrryCz7Y+wnO3byEDb9ux+pjm5HeZ6yhzILd/8D0Laa7C6w9/iWifXoiTBV413qLbpzH5t924X8GNSdweMAjACJGhC2/7cLlyqu4XHEVj4U+hJCu3SGRSODk5ARv7+YMRBs2bMCOHTsANG/M6u3tbfQll8shlUrh7e1tkj/37NmzKC0tRWxsLADAz88P167dwJEjntiypRIiEaDRWC6xg2/NFox0q7TY9TvqsJN1Z5n+NSEEg4JbT4xObAf1DAUsfnwobhbX4Oq5jv+iEYn1cOvyPS79csQCLRMuhhHBu94fHEyTcvf26YGVY+dhwe5/4P09q6BxU2POwy9ibPijhjJXq0txufKq0XmV9dX46tRuvPXIX+5aL8dxeG3rO/j7w9PgLG1es+okkeG9x2bhje2L0dDUiLnDpsPHRYVKvenwd0VFRaeG2BobG/H111/jySefNJzv6uqKESNGIGfeTkgkDZgx0xsymWX/7h1d/Ra+ZxejokkYw/DW6iEODVVh2sNBbRckgmfTWzg5gpryehTOP4jaivZPepHKdZDJtuH6+c7vfG6r+kSOQHB1FN/NuKdqjyasrf3O4vX4+DAICv7Y4vW02O8+G0srhPXax9xiLBYQu3sp8MW0OLjK+U2nSMyDhkkFTuEuw4jnIiESt6/X4KSoh4jb4JCBEACCFNF8N6FtVuo8FRdzkEpNl0dYSmzFPIQ5CWsCiaWGTJUyMVY8HUOB0I5QMLQB6kA3PDS+7e2eXLrUorF2HcpLLLdOUci6+UeAucr/TvZtscRO93ejb+pptboYTodMxno90fYyd0BkGGDRuF4IpvWEdoWCoY3oGeeLyIfunjbM3bsCVdc+Q3XZDSu2SliitY+2XUgARFa8rVZcrLJeZQD8azYiybXKqnW2hzkD4ozhYUiMUN/XNTIyMvD4448b/s8wTKsJFqZOnQqGYZCRkdHqucR8KBjakEEpwdD0MN0bzdP3Om6c+xR11cL7JWQtLkpPyK+1nmxacKwYDE+fZsCyCutVCGBM7Vy4CDAdmTkC4sQBAZgS/4AZW9VMo9Fg7dq1Rjvi1NXVYfXq1SZJ2ollCO8dS+5KxIqQ+FwkvDR/ZDBRaS6h+LfV0DXU89gy/vWLGAM0CmMmY1us2TNsagLE4l7WqxCAovEs0pUnrFpne91PQBwe3hVzRoW3XbAT+vTpA41Gg/Xr1xuOrV+/Ht26dTNsSkAsi4KhjZHKxRg5rRdcPOTw1hTh4vFC6JuEf5/MkkQiFl637ndNphVZef52+U3rTaJp0a8iGyECm0zTojMBMSagC95PjTYkLbeEZ555Bvn5+Ybv8/LykJmZabH6iDEKhjZI4SbDyGnBuHbue76bIgh9IkaAq9Tx3Yx2s2bPEABOn5bD2h91EdeAZ0SrrVpnR3QkIAaqFMhNfxBySesbIJtLWloafvjhB1y4cAEXLlzAnj17kJaWZtE6yR8oGNooD18VnnhtNiRy002JHU2gs7DWtrXJysGwokIPubzt2cjmpqn+FxJdhbs7SnsCospFhlWZsXB3tvxeoyqVCklJSSgoKEB+fj6SkpLg5UWZbayFgqEN8wkKxehXXgcrdtxEQtpuvWxiOcXtrN0zBIC6W/xsNvtEbTaUAtt893b3CoguMjHyM/pC43H3nUPM7ZlnnkFBQQFWrVp1103HiWVQMLRx2qhoJL00AyLWMQNiL00C303oMJHe+h+7CxdMk3tbg6KxCBOVJ3mpu71aC4hKmRirJsUiws+6r1tiYiIaGhrQ2NiI4cOHW7VuR0fB0A4Exw7EyL/OdLiA6OKqgvy65YevLMHaW/38/jsHiaTt3TksoX/FXATJhds7BIwDorOURX5mX/TpZrqMydJYlsXJkyfx66+/miRkJ5ZFwdBOBPcd4HABsX+47SynuNOd2zZZRyQPdQIsV4dn2LW81N0Rh504RAzWIC+jL/pqPdo+wUJcXV3h6urKW/2OihJ125kzB/dh8+IF0DfZzuzKzmBZMcaFzwRXZZvPc5XL92hsNN1Zw5IiIvTo4vGZVeu83Sq3T/BNpfXuv3WUghXh06hADHBXtl2Y2B3qGdqZoL79MerlWXY/qSYm4jGbDYQAPz3DoiIRRCL+NqBNvjUPzhZcp3c/lKwIaygQOjQKhnYo6MF+GPXy62Al9ptRv7ucnyE/cxEx1v/oNTQAUgl/r5uy4TdMdBHebiquYhHW9XoAsRQIHRoFQzv1QEwskme9BZmzdfNSWkOgNga4Zru9QgAWzWRyL5WVWl7qbTGw4i0EyoXza8dHJsHG6GDEuNnf54R0jHDelcTsNOFReOqthVB6ePLdFLPq5TeU7ybcNz56hgBw5gy/9+xYrg6T2M95bUOLUIUcW/oEo6eSElcQCoZ2T9VNi/FzF8HT3z4y37u5dYX0mu3fD2V4mU0KlJbqIZMF8VJ3C231ajziUsdrGwa6K7EpOgh+cttcmkPMj4KhA3D1UiH1rbfhF2a9jV4tpV/PMYDO9idA89UzBICGhjDe6m4xri4HTjwNFY/1dsfaXoFwk9j+H1XEfCgYOgi5Uokn/5aN4NiBfDel01hWCo9q625Wayl83TMEgEsXrb+Y/E4uDb/gaZczVq/3BY03PuwZAClPPXMiXPSOcCBiqRSj/voaYsc8yXdTOuXBqCRw1bY9caYFnz3Dc+cAsZi/ReUtBlXMQYDMOq+DCEB2sB/+HuRr9ew/xDZQMHQwjEiEwX/KwMjpr0Ei42/NWWdoJbY/zNuCz2DIcYBIxP9OHyxXi8mSDRavx13M4tOoQEz2t49RBWIZFAwdVOiAQRifvQjuXW1jU9zgwFjgun30CgFAxHPv5MZ1YfzcA6s+xlCXeotdv4dCjq0PhuBhT0pvRu6NgqEDU3XTYkLOYmh79eG7KW2K8HmI7yaYFT+5Sf9w6pQYDCOMpAwp9Qsgt8A91NHe7tgSEwytk8zs1yb2h4Khg5MrlXjitTmCvo/o0cUX0qv2lcGfAb89w7o6QCaL4LUNLVzrjyPN5b9mu56EYZAd7IcV4VooaOcH0k4UDInhPuKYV9+E3EV4w0mxPUYDTba/nOJ2fN4zbFFdHch3EwwGV8xBNzNMpvGTSfBFdBDdHyQdxv8nkghG0IP9MHHhUvj3EEaPAQDEYincK+0rgw5g/f0MW/Pfs8JJQSbmqjFJsum+rpHg6YrtfUPRh1KrkU6gYEiMuHh6Ydzf52HguAkQCWCIqW/UKHA19jNxpoUQeoZXr3KQyQL4boZBUFU+HnJp6PB5SlaEd0M1+DQqEB60kJ50Ev+fSCI4IhGLAU+OR2rW2+ji48trW7qJ+c+WYgl8zyZtodMJa7nKU/VvQ9aB12aAuwK7+oZigq/9jR4Q66JgSO7KJygUTy9YiqiERICHX94hQf3tajnF7YTQMwSAK5eFFUTc6n/Cn1wvtFlOLmLwVpAv1vcOQjeaLUrMQBifSCJYErkcw56dhpS/56CLj59V647oOsSq9VmTiOfZpC3OnGHAssKaNBVf8Xf432MyTS8XJ3zzYCie03ib7d5rRkYGGIbBggULjI5v3LjRUMd3330HhmHAMAxEIhHc3NwQHR2NGTNmoLi42HCOVqs1lGvtKyMjwyxtJuZFwZC0i6ZnJCa+/QH6jU2BiLX8fRlPD39Irtrv21MIE2gAQK8HxGL+s9HcTsxVYZL0K5PjUobBq1o1vuwTghCF+bMnyeVyLFy4EDdv3rxnuVOnTuHKlSs4ePAgZs6ciR07diAiIgInTpwAABw8eBDFxcUoLi7Gv//9b8M5Lcfef/99s7ed3D/7/W1DzE4slWJQ6kSkzV8MdVCIRevqGzbK7pZT3E4ow6QAUFbmz3cTTIRUrsQgl0bD93HuSuzsG4pXuqshtlCS84SEBKjVasyfP/+e5by9vaFWqxESEoLU1FTs2bMHKpUKU6ZMAQCoVCqo1Wqo1Wp4eHgYnaNWq+Hm5maR9pP7I5xPJLEZqoDu+NPcRRia/iwkcvNvjCqRyOFewX8iaUvie9H97YpOScGA/5nDd0qtXwQ/mQTLe3TDv6ODEGyB3uDtWJZFTk4OPvjgA1y6dKnd5zk5OeH555/Hnj17cO3aNQu2kFgSBUPSKYxIhD6PjcEzi/8X4Q89YtYJNrFRo8HV2ufEmRZCuWcIANXVHGTyHnw3wwjDSBDlHYUfY7VIVlvvD6OxY8eid+/emD17dofOCwtrnvV8/vx5C7SKWAMFwztkZGTg8ccfv2eZS5cuQSqVIiKi9cXpTU1NWLx4MSIjIyGXy9GlSxeMGDECe/bsabX8qlWrMGjQIABAfHw8pk+f3mY79+7dC5ZlkZSUZPLY+fPn73rzft++fW1euyOUHp5IfOGvSMtZDP+e5lms7y8KNst1hEwo9wxb1NYG8d0EAw+PwegX+yVCgv8Gmdj6C+gXLlyIVatW4eTJk+0+h+Oah/SF9nMl7UfBsBMKCgqQkpKCyspK7N+/3+gxjuOQmpqKrKwsvPTSSzh58iS+++47aDQaxMfHY+PGjSbX++KLLzB69OgOtSE3Nxcvvvgivv/+e1y5cqXVMjt27DDctG/5iomJ6VA97dU1MAhPzV6A0a+8Dnd153dECAseBNyw714hIKyeIQCcP8//jFKFIhhRkf9AdO8CKBQP8NaOIUOGYPjw4Zg1a1a7z2kJnFqt1kKtIpZG6Ro6iOM45Ofn48MPP4S/vz9yc3PRr18/w+OFhYX417/+hU2bNmHUqFGG4ytWrEBpaSkmT56MYcOGQaFo/ou3rq4O33zzDXJyctrdhurqaqxbtw6HDh1CSUkJCgoK8Prrr5uU8/T0hFqtvo9n23HBsQMR2Kcvftq6BfvXr0NdTXWHzg/3jgNaj+12RWjB8PIlDmFhvmhosP6L7+SkRWD3l9C160gwAplYtGDBAvTu3RuhoaFtlr116xZWrFiBIUOGQKWinKi2ShjvPBvy7bffora2FgkJCUhLS8PatWtRU1NjeHz16tUICQkxCoQtXnnlFZSWlmL79u2GYzt37oSfn5/hnkN7FBYWIiwsDKGhoUhLS0NeXp5hmEYIWLEED44ci8nL8hCXkga50qVd56m8tBCXCCtIWAojwI8epw+3an1yuR96hM1H/37boFaPFkwgBIDIyEhMmDABS5cuNXns2rVrKCkpwenTp7F27VrExcXhxo0b+Oijj3hoKTEX4bz7bERubi5SU1PBsiwiIiIQGBiIzz//3PB4UVERevRofTJCy/GioiLDsc4OkaalpQEAEhMTUVFRgd27d5uUGzhwIJRKpdGXNcmcndE/ORXPLsvFoNSJbe6I0TckCdALJ6hbktB6hgBQUuJtlXqkUm+EhMzBgP474OubApFImANUWVlZ0Ov1JsdDQ0Ph6+uLmJgYLFiwAAkJCfj555/Rs6ewUtuRjhHmu1CgysvLsX79evzwww+GY2lpacjNzTXKKtHeXhrHcdi8eTMKCwvb3YZTp07hwIED2LBhAwBALBbjqaeeQm5uLuLj443Krlu37q6B2ZqkTs7oNzYF0SNG4aetW3B4ywbcqqo0LiNxgmu5Gzg08dRK6xLS0ooWRUUixHk7Q6+vtcj1pVIVunWbDH+/NLCsZZdJdFRBQYHJMa1Wi/r6esP38fHxHR6B6cw5hB8UDDtg9erVqKurM7pHyHEc9Ho9ioqKEBISgpCQkLvOQms5HhLSvGD9wIED0Ol0GDhwYLvbkJubC51OB1/fPxJocxwHmUyGZcuWGS3o1Wg0CAoSzixBqdwJ/R4fh+jEkTi+/Wsc2boZVTeuAwBie40BV+oYgRAQZs9QpwOkkijU1Zt3xrFCEYxumslQq0dDJJKa9dqEmAsNk3ZAbm4uXnnlFRw9etTwdezYMQwePBh5eXkAgNTUVJw+fRqbN282Of/dd9+Fp6cnhg0bBqB5iDQpKQlsO7dK0ul0+Pjjj/Huu++atMHX1xdr1qwx35O1IKncCQ+OegKTl/4TI6fPhE9wKPwhnKBtDULsGQJAeUU3M12JgYfHYPTulYf+/bbC1/dJCoRE0Khn2IqKigocPXrU6FhVVRWOHDmCzz77zGSyy/jx45GVlYXs7Gykpqbi888/R3p6Ot555x088sgjqKysxPLly7Fp0yZ8/vnnhpmkmzZtQlZWlkn9169fN6nfx8cHe/fuxc2bNzFp0iSTlE7JycnIzc3F888/bzhWWlqKkpISo3Lu7u6Qy4UxRCViWYQOGIzQAYPRcLEK1XuvoPb4dUBn/8NKQuwZAsDpIjkioxgAnfsZsKwCPuon4O8/EQpFoHkbR4gFMRwNaBvJyMjAqlWrWj1+4MAB/PLLLyaPlZSUwM/PDxs2bMDo0aOh0+mwZMkSFBQU4PTp05DL5RgwYADefPNNxMXFAQDOnj2L8PBwlJaWGoIj0HyPobXJMHPnzsX+/fuh1+vx5Zdfmjx+4MAB9OvXD8eOHYOrqyu6d+/e6vNbs2YNUlNT2/16WFtTTSNqD5Wg5tBV6K7f4rs5FnMy+Cb2XDzCdzNaNWzYAdTVn+rQOS4ukfDxeQI+6rEQi9s3e5gQIaFgyJP33nsPO3bswFdfmWbnJ80aLlah5shV3Dp2HXo7S892Kqgc/7l0mO9mtGrwkHIApsP8d5LJfKBWPw4f9VheF8kTYg40TMoTf3//DmW4cERSjQukGhe4JwWi7rcy1By5hrpTZXaxm4VQ7xkCwMXf3aG5y61DllXCWzUcavXj6NJlAKUfI3aDeobEpjTVNKLul1Lc+rUUdWfKAZ3pOjBbcCaoEt9dOsh3M+6Cw8OPbENjY/NMX5FIDg+POHT1ToJK9ShY1vw7lRDCN+oZEpvCKiRQxKqhiFVD39CEulM3UfdrKepOldnUUCoj6D9BGbCigfBSi6FSJcDDYzAFQGL3KBgSmyWSsnCO9IJzpBe4Jg715ytQd7IM9WfL0VhS09kJkVbBcMIaXmQYBr6+vggODkZQUBB8fX0hEtHKK+I4KBgSu8CwDOQPuEP+gDsAQF/biPpzFag/W4H6/5aj8WqtoIIj30srRCIR1Go1NBoNNBoNunfvbjSrmRBHQ3/6EbskcpbAKdwL7qMfQNfpMfD5Wz94TAiDcpAfpFpXMFJ+3/oiK/cMFQoFwsLCkJCQgMzMTMyaNQt//vOfMWLECERERPASCG/fOzQjI8Ow56ZEIkH37t0xY8YM1NXVGZ1z+96cYrEY3bp1w8svv2yUNq2goMConFKpRExMDNavX290rfj4eKNyXbt2xbhx43DhwgWLP3ciPNQzJA6BVUrhHKmCc2TzFjucnoPuWi0aLlej8Uo1Gotr0HClBlydde47WnKY1MXFBSqVCiqVCn5+fvD394eHh/V2i++sxMRE5Ofno7GxEYcPH0Z6ejoYhsHChQuNyuXn5yMxMRGNjY04duwYMjMzoVAoMHfuXEMZV1dXnDrVvFayqqoK+fn5SElJwS+//GK0LdOzzz6LrKwscByHCxcuYPr06UhLS8N//vMf6zxpIhgUDIlDYkQMJGoFJGoFENPVcLypugG60jrobtyCrvTW///b/D1Xb77cqffbL5XJZPDw8DD68vLygkqlEkyGoY6SyWSG/Tc1Gg0SEhKwfft2k2Do7u5uVG7MmDE4csQ4gQHDMIYyarUa2dnZWLRoEY4fP24UDJ2dnQ3lfHx8MG3aNDz33HMWe45EuCgYEnIbVikFq5RCFmC63VRTTSP01Q1oqv7/f6saoa9uRFN1A/TVjdDf0oFraAKn04Nr0INrbALXqAen05vcr2S45nDIsixYloVYLAbLsnBycoJCoYBCoYCzs7Ph/3d+7+Rk37M7f/75Z/z4448ICAi4Z7mioiLs2rXLaNeYOzU1NeHjjz8GAPTp0+eu5crKylBYWGiUiJ84DgqGhLQTq5CAVUgg6dp22TtxjXpwHAeGAcAw8AUQKx5m7ibatC1btkCpVEKn06G+vh4ikQjLli0zKTd+/HiwLGsoN3LkSJMEFhUVFYb9O2/dugWJRIIVK1bggQeMM+V8+OGH+Oc//wmO41BbW4uQkBBs27bNck+SCBZNoCHEChiJCCIpC0bCghGLIBLTR+9OQ4cOxdGjR7F//36kp6cjMzMTycnJJuUWL15s2K1ly5YtKCoqwtNPP21UxsXFxbCry08//YScnBw8//zzJrvJTJgwwXCtH374AUFBQXj00UdRVVVl0edKhId6hoQQQVAoFIb9N/Py8tCrVy/k5uZi0qRJRuXUarWhXGhoKKqqqjB+/HhkZ2cbjotEIqO9PKOiovDNN99g4cKFGDVqlOG4m5uboVxQUBByc3Ph4+ODdevWYfLkyRZ9vkRY6M9TQojgiEQivP7663jjjTdw69a9dy9p2Q+0PeXMdS1ifygYEkIEady4cWBZFsuXLzc6Xl5ejpKSEly5cgW7d+9GVlYWQkJC0KNHD0MZjuNQUlKCkpISnDt3DitWrMC2bdswZswYo2vV1tYayh07dgxTpkyBXC7Ho48+apXnSISDhkkJIYIkFosxbdo0vP3225gyZYohMUBmZiaAP5ZPDBkyBDk5ORCL//h1VllZCR8fHwDNSzYCAgKQlZWFmTNnGtWxcuVKrFy5EgDQpUsXREVF4auvvjJafkEcA+1aQayqZfPk+fPn47XXXjMc37hxI8aOHYuWt+PKlSuxbNkynD17FmKxGN27d0dKSgpmzZoFrVZ7zywh6enpKCgosPRTIYTYEeoZEquTy+VYuHAhnnvuOXTp0sXk8by8PEyfPh1Lly7FQw89hPr6ehw/fhw///wzAODgwYNoampeAP/jjz8iOTkZp06dgqtr89pAe1+DRwgxP7pnSKwuISEBarUa8+fPb/XxTZs2ISUlBZMmTUJQUBDCw8Mxfvx4zJs3DwCgUqmgVquhVqsNaca8vb0Nx9zc3Kz2XISupKQEL774IgIDAyGTyaDRaDBq1Cjs3LkTAKDVarFkyZJWzz1//rxR7k6pVIqgoCBkZ2fj9gGlOXPmGJVzc3PD4MGDsXv3bqPrabVaQxmWZeHr64tJkybh5s2bFnv+hLQXBUNidSzLIicnBx988AEuXbpk8rharca+ffsoYfJ9On/+PGJiYrBr1y688847OHHiBLZu3YqhQ4di6tSp7b7Ojh07UFxcjNOnT+Ott97CvHnzkJeXZ1QmPDwcxcXFKC4uxt69exEcHIyRI0eioqLCqFxWVhaKi4vx+++/47PPPsP333+Pv/zlL2Z5voTcDwqGhBdjx45F7969MXv2bJPHZs+eDXd3d2i1WoSGhiIjIwOFhYXQ621zV3u+vPDCC2AYBgcOHEBycjJCQkIQHh6Ol19+Gfv27Wv3dTw9PaFWqxEQEIAJEyYgLi7OJBeoWCw29Mx79uyJrKwsVFdXo6ioyKici4sL1Go1/Pz8MHToUKSnp5tcixA+UDAkvFm4cCFWrVqFkydPGh338fHB3r17ceLECbz00kvQ6XRIT09HYmIiBcR2Kisrw9atWzF16tRWt2dyd3fv1HUPHTqEw4cP3zN/Z319PfLz8+Hu7n7PWZmXL1/G5s2bKRcoEQQKhoQ3Q4YMwfDhw03ySraIiIjACy+8gE8//RTbt2/H9u3bTe5DkdadOXMGHMchLCzsvq81cOBAKJVKSKVS9O3bFykpKZg4caJRmRMnTkCpVEKpVMLJyQmLFi3CmjVrDJOaWsycOdNQxt/fHwzD4L333rvvNhJyvygYEl4tWLAAmzdvxt69e+9ZrmfPngCAmpoaazTL5plzxdS6desM+TsLCwvxxRdfGC2LAZrTorXkAj18+DCmTJmCcePG4dChQ0blXn31VRw9ehTHjx83TOJJSkoyzA4mhC+0tILwKjIyEhMmTMDSpUsNx6ZMmQJfX188/PDD8Pf3R3FxMbKzs6FSqTBgwAAeW2s7goODwTAMfvvtt/u+lkajMeTv7NGjB86ePYs333wTc+bMMeyd2DLTtEV0dDQ2btyIJUuW4NNPPzUc9/LyMpQLDg7GkiVLMGDAAHz77bdISEi477YS0lnUMyS8y8rKMroXmJCQgH379mHcuHEICQlBcnIy5HI5du7cCU9PTx5bajs8PDwwfPhwLF++vNXedHl5eaev3bJ9UkNDQ5vlKBcosRXUMyRW1VpmGK1Wi/r6esP3ycnJrW7d05r4+HizDgnak+XLlyMuLg6xsbHIyspCVFQUdDodtm/fjo8++sgwceny5cs4evSo0bm3b6pbWlqKkpIS6HQ6nDhxAu+//z6GDh1qdD9Qp9OhpKQEAFBVVYV169bh119/NUl/VlVVhZKSEnAch4sXL2LGjBlQqVQYOHCghV4FQtqJI4TYrStXrnBTp07lAgICOKlUyvn5+XGjR4/mvv32W47jOC4gIIADYPL1ySefcOfOnTM6xrIs5+/vzz377LPctWvXDHXMnj3bqJyzszMXGRnJffTRR0ZtubMulUrFPfbYY9xPP/1kxVeEkNZRblJid3JycpCTk9PqY4MHD8bXX39t5RYRQoSOgiGxO2VlZSgrK2v1MScnJ/j5+Vm5RYQQoaNgSAghxOHRbFJCCCEOj4IhIYQQh0fBkBBCiMOjYEgIIcThUTAkhBDi8CgYEkIIcXgUDAkhhDg8CoaEEEIcHgVDQgghDo+CISGEEIdHwZAQQojDo2BICCHE4VEwJIQQ4vAoGBJCCHF4FAwJIYQ4PAqGhBBCHB4FQ0IIIQ6PgiEhhBCHR8GQEEKIw6NgSAghxOFRMCSEEOLwKBgSQghxeBQMCSGEODwKhoQQQhweBUNCCCEOj4IhIYQQh/d/oazkg+jumHUAAAAASUVORK5CYII="},"metadata":{}}]},{"cell_type":"code","source":"ecg_df_HTV_diagnostic_n[\"diagnostic\"],uniques_diagnostic = pd.factorize(ecg_df_HTV_diagnostic_n.diagnostic)\necg_df_HTV_diagnostic= ecg_df_HTV_diagnostic_n.dropna()\necg_df_HTV_diagnostic.index = pd.RangeIndex(start=0, stop=len(ecg_df_HTV_diagnostic))\n","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:39.301134Z","iopub.execute_input":"2024-06-23T10:55:39.301773Z","iopub.status.idle":"2024-06-23T10:55:39.374511Z","shell.execute_reply.started":"2024-06-23T10:55:39.301737Z","shell.execute_reply":"2024-06-23T10:55:39.373630Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"ecg_df_HTV_diagnostic.diagnostic.value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:39.375778Z","iopub.execute_input":"2024-06-23T10:55:39.376110Z","iopub.status.idle":"2024-06-23T10:55:39.389330Z","shell.execute_reply.started":"2024-06-23T10:55:39.376082Z","shell.execute_reply":"2024-06-23T10:55:39.388479Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"diagnostic\n0     9025\n1     2506\n2     2451\n3     2092\n4     2045\n5     2010\n6     1972\n7     1278\n8     1168\n9     1154\n10    1121\n11    1017\n12     594\n13     521\n14     419\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"#ecg_df_HTV_diagnostic_n[\"diagnostic\"],uniques_diagnostic = pd.factorize(ecg_df_HTV_diagnostic_n.diagnostic)\necg_df_HTV_diagnostic= ecg_df_HTV_diagnostic_n.dropna()","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:39.394622Z","iopub.execute_input":"2024-06-23T10:55:39.395050Z","iopub.status.idle":"2024-06-23T10:55:39.454566Z","shell.execute_reply.started":"2024-06-23T10:55:39.395019Z","shell.execute_reply":"2024-06-23T10:55:39.453472Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"pd.DataFrame(uniques_diagnostic)","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:39.455723Z","iopub.execute_input":"2024-06-23T10:55:39.456071Z","iopub.status.idle":"2024-06-23T10:55:39.465729Z","shell.execute_reply.started":"2024-06-23T10:55:39.456041Z","shell.execute_reply":"2024-06-23T10:55:39.464717Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"           0\n0       NORM\n1        LVH\n2       LAFB\n3      CRBBB\n4       1AVB\n5    LAO/LAE\n6       NST_\n7      CLBBB\n8      IRBBB\n9        NDT\n10       IMI\n11       RVH\n12     ISCAL\n13  ISC_,LVH\n14      LPFB","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NORM</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>LVH</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LAFB</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CRBBB</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1AVB</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>LAO/LAE</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>NST_</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CLBBB</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>IRBBB</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>NDT</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>IMI</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>RVH</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>ISCAL</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>ISC_,LVH</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>LPFB</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_numeric = ecg_df_HTV_diagnostic.select_dtypes(include=[np.number])\nnumeric_cols = df_numeric.columns.values","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:39.466954Z","iopub.execute_input":"2024-06-23T10:55:39.467299Z","iopub.status.idle":"2024-06-23T10:55:39.517738Z","shell.execute_reply.started":"2024-06-23T10:55:39.467260Z","shell.execute_reply":"2024-06-23T10:55:39.516942Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"df_corr_diagnostic_class_n =ecg_df_HTV_diagnostic[numeric_cols].corr(method='spearman')[[\"diagnostic\"]] #,\"diagnostic\"","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:55:39.518874Z","iopub.execute_input":"2024-06-23T10:55:39.519205Z","iopub.status.idle":"2024-06-23T10:56:07.373620Z","shell.execute_reply.started":"2024-06-23T10:55:39.519177Z","shell.execute_reply":"2024-06-23T10:56:07.372754Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"code","source":"#ecg_df_HTV_new = ecg_df_HTV_new.dropna(thresh=260).dropna(axis=1)","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:56:07.374947Z","iopub.execute_input":"2024-06-23T10:56:07.375848Z","iopub.status.idle":"2024-06-23T10:56:07.380056Z","shell.execute_reply.started":"2024-06-23T10:56:07.375807Z","shell.execute_reply":"2024-06-23T10:56:07.379044Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"def func_corr_a(name,df_corr_diagnostic,N):\n    param = df_corr_diagnostic[((df_corr_diagnostic[name] > N)|(df_corr_diagnostic[name] < -N))&(df_corr_diagnostic[name] != 1)  ]\n    list_corr_=[]\n    for i in list(param.index):\n        if '_' in i :\n            if (i[i.index('_')+1:] in list_corr_) != True:\n                list_corr_.append(i)\n                list_corr_.append(i[i.index('_')+1:])\n                list_corr_.append(param[name][i])\n            else :\n                if abs(list_corr_[list_corr_.index(i[i.index('_')+1:])+1]) < abs(param[name][i] ):\n                    list_corr_[list_corr_.index(i[i.index('_')+1:])-1] = i   \n                    list_corr_[list_corr_.index(i[i.index('_')+1:])+1] = param[name][i]\n        else : \n            list_corr_.append(i)\n            list_corr_.append(i)\n            list_corr_.append(param[name][i])\n    return list_corr_","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:56:07.381345Z","iopub.execute_input":"2024-06-23T10:56:07.381698Z","iopub.status.idle":"2024-06-23T10:56:07.393754Z","shell.execute_reply.started":"2024-06-23T10:56:07.381666Z","shell.execute_reply":"2024-06-23T10:56:07.392937Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"list_corr_diagnostic=func_corr_a(\"diagnostic\",df_corr_diagnostic_class_n,0.01)\nlist_corr_diagnostic[::3]","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:56:07.394838Z","iopub.execute_input":"2024-06-23T10:56:07.395125Z","iopub.status.idle":"2024-06-23T10:56:07.470058Z","shell.execute_reply.started":"2024-06-23T10:56:07.395102Z","shell.execute_reply":"2024-06-23T10:56:07.469171Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"['10_Mean RR (ms)',\n '8_STD RR/SDNN (ms)',\n \"10_Mean HR (Kubios' style) (beats/min)\",\n '10_Mean HR (beats/min)',\n '8_STD HR (beats/min)',\n '0_Min HR (beats/min)',\n '10_Max HR (beats/min)',\n '8_RMSSD (ms)',\n '8_NNxx',\n '8_pNNxx (%)',\n '11_VLF_Peak (Hz)',\n '10_LF_Peak (Hz)',\n '0_VLF_Abs (ms2)',\n '0_LF_Abs (ms2)',\n '0_HV_Abs (ms2)',\n '4_VLF_Rel (%)',\n '11_LF_Rel (%)',\n '0_VLF_Log (-)',\n '0_LF_Log (-)',\n '0_HV_Log (-)',\n '0_Total Power (ms)',\n '9_Max_R',\n '9_Min_R',\n '0_Max_RR',\n '10_Min_RR',\n '3_Max_P',\n '9_Min_P',\n '6_Max_Q',\n '8_Min_Q',\n '0_Max_S',\n '0_Min_S',\n '10_Max_T',\n '10_Min_T',\n '11_LF_Norm (-)',\n '11_HV_Norm (-)',\n '11_LF/HF (-)',\n '3_HV_Peak (Hz)',\n '3_HV_Rel (%)',\n 'age',\n 'sex']"},"metadata":{}}]},{"cell_type":"code","source":"wew_twe = list_corr_diagnostic[::3].copy()[:-2]\nwew = []\nA = \" отведение \"\nfor i in range(len(wew_twe)):\n    wew.append([int(wew_twe[i].split(\"_\")[0])+1,list_corr_diagnostic[1::3][i]])\nwew.sort()","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:56:07.471103Z","iopub.execute_input":"2024-06-23T10:56:07.471393Z","iopub.status.idle":"2024-06-23T10:56:07.477210Z","shell.execute_reply.started":"2024-06-23T10:56:07.471344Z","shell.execute_reply":"2024-06-23T10:56:07.476346Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"for i in pd.DataFrame(wew).index:\n    print(pd.DataFrame(wew)[0][i],A,pd.DataFrame(wew)[1][i])","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:56:07.478329Z","iopub.execute_input":"2024-06-23T10:56:07.478595Z","iopub.status.idle":"2024-06-23T10:56:07.508922Z","shell.execute_reply.started":"2024-06-23T10:56:07.478569Z","shell.execute_reply":"2024-06-23T10:56:07.507935Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stdout","text":"1  отведение  HV_Abs (ms2)\n1  отведение  HV_Log (-)\n1  отведение  LF_Abs (ms2)\n1  отведение  LF_Log (-)\n1  отведение  Max_RR\n1  отведение  Max_S\n1  отведение  Min HR (beats/min)\n1  отведение  Min_S\n1  отведение  Total Power (ms)\n1  отведение  VLF_Abs (ms2)\n1  отведение  VLF_Log (-)\n4  отведение  HV_Peak (Hz)\n4  отведение  HV_Rel (%)\n4  отведение  Max_P\n5  отведение  VLF_Rel (%)\n7  отведение  Max_Q\n9  отведение  Min_Q\n9  отведение  NNxx\n9  отведение  RMSSD (ms)\n9  отведение  STD HR (beats/min)\n9  отведение  STD RR/SDNN (ms)\n9  отведение  pNNxx (%)\n10  отведение  Max_R\n10  отведение  Min_P\n10  отведение  Min_R\n11  отведение  LF_Peak (Hz)\n11  отведение  Max HR (beats/min)\n11  отведение  Max_T\n11  отведение  Mean HR (Kubios' style) (beats/min)\n11  отведение  Mean HR (beats/min)\n11  отведение  Mean RR (ms)\n11  отведение  Min_RR\n11  отведение  Min_T\n12  отведение  HV_Norm (-)\n12  отведение  LF/HF (-)\n12  отведение  LF_Norm (-)\n12  отведение  LF_Rel (%)\n12  отведение  VLF_Peak (Hz)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Z","metadata":{}},{"cell_type":"code","source":"def stardant(X_columns):\n    #определяем смещение Центрируем данные относительно 0\n    fix_displacement_min = X_columns.describe()\n    for i in X_columns.describe().columns:   \n        X_columns[i] = X_columns[i] - fix_displacement_min[i][\"min\"]\n    fix_displacement = X_columns.describe()\n    #Стандартизация по z-оценке\n    for i in X_columns.describe().columns:   \n        X_columns[i] = (X_columns[i] - fix_displacement[i][\"mean\"])/(fix_displacement[i][\"std\"])\n    return X_columns ,fix_displacement_min,fix_displacement#","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:56:07.510022Z","iopub.execute_input":"2024-06-23T10:56:07.510264Z","iopub.status.idle":"2024-06-23T10:56:07.518866Z","shell.execute_reply.started":"2024-06-23T10:56:07.510242Z","shell.execute_reply":"2024-06-23T10:56:07.517942Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"markdown","source":"male 0, female 1","metadata":{}},{"cell_type":"markdown","source":"## diagnostic","metadata":{}},{"cell_type":"code","source":"ecg_df_HTV_diagnostic_n_new = ecg_df_HTV_diagnostic.copy()","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:56:07.519807Z","iopub.execute_input":"2024-06-23T10:56:07.520089Z","iopub.status.idle":"2024-06-23T10:56:07.569717Z","shell.execute_reply.started":"2024-06-23T10:56:07.520066Z","shell.execute_reply":"2024-06-23T10:56:07.568824Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"df_numeric = ecg_df_HTV_diagnostic.select_dtypes(include=[np.number])\nnumeric_cols = df_numeric.columns.values","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:56:07.571143Z","iopub.execute_input":"2024-06-23T10:56:07.571858Z","iopub.status.idle":"2024-06-23T10:56:07.617143Z","shell.execute_reply.started":"2024-06-23T10:56:07.571823Z","shell.execute_reply":"2024-06-23T10:56:07.616387Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"df_test = ecg_df_HTV_diagnostic_n_new.dropna().copy()[numeric_cols]\ndf_new = ecg_df_HTV_diagnostic_n_new.dropna().copy()[numeric_cols]","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:56:07.618322Z","iopub.execute_input":"2024-06-23T10:56:07.618674Z","iopub.status.idle":"2024-06-23T10:56:07.876535Z","shell.execute_reply.started":"2024-06-23T10:56:07.618640Z","shell.execute_reply":"2024-06-23T10:56:07.875643Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"code","source":"n = 3000\ndf_new =df_test[df_test.diagnostic == df_test.diagnostic.value_counts().index[0]][:n ].copy()\nfor i in df_test.diagnostic.value_counts().index[1:]:\n    df_new = pd.concat([df_new, df_test[df_test.diagnostic == i][:n ]], ignore_index=True)","metadata":{"execution":{"iopub.status.busy":"2024-06-23T11:13:09.245788Z","iopub.execute_input":"2024-06-23T11:13:09.246190Z","iopub.status.idle":"2024-06-23T11:13:09.467783Z","shell.execute_reply.started":"2024-06-23T11:13:09.246157Z","shell.execute_reply":"2024-06-23T11:13:09.466769Z"},"trusted":true},"execution_count":165,"outputs":[]},{"cell_type":"code","source":"#X_diagnostic, y_diagnostic  = df_new[df_new.columns[:-1]], df_new['diagnostic']\nX_diagnostic, y_diagnostic  = df_new[list_corr_diagnostic[::3]], df_new['diagnostic']\nlist_corr_diagnostic[::3]\ny_diagnostic= y_diagnostic.astype('int')\ndf_new['diagnostic'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-23T11:13:47.055918Z","iopub.execute_input":"2024-06-23T11:13:47.056319Z","iopub.status.idle":"2024-06-23T11:13:47.067737Z","shell.execute_reply.started":"2024-06-23T11:13:47.056287Z","shell.execute_reply":"2024-06-23T11:13:47.066832Z"},"trusted":true},"execution_count":170,"outputs":[{"execution_count":170,"output_type":"execute_result","data":{"text/plain":"diagnostic\n0     3000\n1     2506\n2     2451\n3     2092\n4     2045\n5     2010\n6     1972\n7     1278\n8     1168\n9     1154\n10    1121\n11    1017\n12     594\n13     521\n14     419\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"# import warnings filter\nfrom warnings import simplefilter\n# ignore all future warnings\nsimplefilter(action='ignore', category=FutureWarning)","metadata":{"execution":{"iopub.status.busy":"2024-06-23T11:01:05.472563Z","iopub.execute_input":"2024-06-23T11:01:05.472940Z","iopub.status.idle":"2024-06-23T11:01:05.477261Z","shell.execute_reply.started":"2024-06-23T11:01:05.472912Z","shell.execute_reply":"2024-06-23T11:01:05.476320Z"},"trusted":true},"execution_count":84,"outputs":[]},{"cell_type":"code","source":"sm = KMeansSMOTE(random_state=42,cluster_balance_threshold = 0.01)\nX_diagnostic, y_diagnostic = sm.fit_resample(X_diagnostic, y_diagnostic)\nsm = BorderlineSMOTE(random_state=42)\nX_diagnostic, y_diagnostic = sm.fit_resample(X_diagnostic, y_diagnostic)","metadata":{"execution":{"iopub.status.busy":"2024-06-23T11:13:58.617885Z","iopub.execute_input":"2024-06-23T11:13:58.618784Z","iopub.status.idle":"2024-06-23T11:14:08.561195Z","shell.execute_reply.started":"2024-06-23T11:13:58.618746Z","shell.execute_reply":"2024-06-23T11:14:08.560218Z"},"trusted":true},"execution_count":172,"outputs":[]},{"cell_type":"code","source":"X_train, X_valid, y_train, y_valid = train_test_split(X_diagnostic, y_diagnostic, test_size=0.1, random_state=0)","metadata":{"execution":{"iopub.status.busy":"2024-06-23T11:14:08.562763Z","iopub.execute_input":"2024-06-23T11:14:08.563095Z","iopub.status.idle":"2024-06-23T11:14:08.588662Z","shell.execute_reply.started":"2024-06-23T11:14:08.563068Z","shell.execute_reply":"2024-06-23T11:14:08.587878Z"},"trusted":true},"execution_count":173,"outputs":[]},{"cell_type":"markdown","source":"## LGBMClassifier","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import f1_score,accuracy_score,balanced_accuracy_score","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:56:35.018632Z","iopub.execute_input":"2024-06-23T10:56:35.019006Z","iopub.status.idle":"2024-06-23T10:56:35.023544Z","shell.execute_reply.started":"2024-06-23T10:56:35.018957Z","shell.execute_reply":"2024-06-23T10:56:35.022636Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import classification_report","metadata":{"execution":{"iopub.status.busy":"2024-06-23T10:56:36.115523Z","iopub.execute_input":"2024-06-23T10:56:36.116441Z","iopub.status.idle":"2024-06-23T10:56:36.121023Z","shell.execute_reply.started":"2024-06-23T10:56:36.116402Z","shell.execute_reply":"2024-06-23T10:56:36.119881Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"code","source":"model_LGBMClassifier = LGBMClassifier(random_seed=7575)\nmodel_LGBMClassifier.fit(X_train, y_train)\nprint(\"score \",model_LGBMClassifier.score(X_train, y_train))\ny_predict = model_LGBMClassifier.predict(X_valid)\nprint(\"f1_score micro \",f1_score(y_valid,y_predict, average='micro')) #\"samples\"\nprint(\"f1_score macro \",f1_score(y_valid,y_predict, average='macro'))\nprint(\"f1_score weighted \",f1_score(y_valid,y_predict, average='weighted'))","metadata":{"execution":{"iopub.status.busy":"2024-06-18T13:35:42.575043Z","iopub.execute_input":"2024-06-18T13:35:42.575762Z","iopub.status.idle":"2024-06-18T13:36:03.987248Z","shell.execute_reply.started":"2024-06-18T13:35:42.575727Z","shell.execute_reply":"2024-06-18T13:36:03.986246Z"},"trusted":true},"execution_count":74,"outputs":[{"name":"stdout","text":"[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.016519 seconds.\nYou can set `force_col_wise=true` to remove the overhead.\n[LightGBM] [Info] Total Bins 9981\n[LightGBM] [Info] Number of data points in the train set: 40567, number of used features: 40\n[LightGBM] [Info] Start training from score -2.703059\n[LightGBM] [Info] Start training from score -2.714530\n[LightGBM] [Info] Start training from score -2.710815\n[LightGBM] [Info] Start training from score -2.718258\n[LightGBM] [Info] Start training from score -2.709333\n[LightGBM] [Info] Start training from score -2.705269\n[LightGBM] [Info] Start training from score -2.708593\n[LightGBM] [Info] Start training from score -2.708223\n[LightGBM] [Info] Start training from score -2.708223\n[LightGBM] [Info] Start training from score -2.707114\n[LightGBM] [Info] Start training from score -2.705637\n[LightGBM] [Info] Start training from score -2.707853\n[LightGBM] [Info] Start training from score -2.704900\n[LightGBM] [Info] Start training from score -2.698287\n[LightGBM] [Info] Start training from score -2.710815\nscore  0.9801809352429315\nf1_score micro  0.8524844720496895\nf1_score macro  0.8529620013502587\nf1_score weighted  0.8530224235260528\n","output_type":"stream"}]},{"cell_type":"code","source":"accuracy_score(y_valid, y_predict),balanced_accuracy_score(y_valid, y_predict)","metadata":{"execution":{"iopub.status.busy":"2024-06-18T13:36:03.989433Z","iopub.execute_input":"2024-06-18T13:36:03.989869Z","iopub.status.idle":"2024-06-18T13:36:03.998954Z","shell.execute_reply.started":"2024-06-18T13:36:03.989836Z","shell.execute_reply":"2024-06-18T13:36:03.998017Z"},"trusted":true},"execution_count":75,"outputs":[{"execution_count":75,"output_type":"execute_result","data":{"text/plain":"(0.8524844720496895, 0.8529672154495437)"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(y_valid, y_predict))","metadata":{"execution":{"iopub.status.busy":"2024-06-18T13:36:04.000079Z","iopub.execute_input":"2024-06-18T13:36:04.000361Z","iopub.status.idle":"2024-06-18T13:36:04.015752Z","shell.execute_reply.started":"2024-06-18T13:36:04.000339Z","shell.execute_reply":"2024-06-18T13:36:04.014992Z"},"trusted":true},"execution_count":76,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.63      0.75      0.68       287\n           1       0.83      0.74      0.78       318\n           2       0.75      0.70      0.72       308\n           3       0.90      0.88      0.89       328\n           4       0.89      0.84      0.86       304\n           5       0.87      0.90      0.88       293\n           6       0.89      0.89      0.89       302\n           7       0.84      0.84      0.84       301\n           8       0.87      0.84      0.85       301\n           9       0.93      0.93      0.93       298\n          10       0.72      0.76      0.74       294\n          11       0.90      0.90      0.90       300\n          12       0.88      0.95      0.91       292\n          13       0.93      0.91      0.92       274\n          14       0.99      0.99      0.99       308\n\n    accuracy                           0.85      4508\n   macro avg       0.85      0.85      0.85      4508\nweighted avg       0.86      0.85      0.85      4508\n\n","output_type":"stream"}]},{"cell_type":"code","source":"print(classification_report(df_test['diagnostic'], model_LGBMClassifier.predict( df_test[X_train.columns])))","metadata":{"execution":{"iopub.status.busy":"2024-06-18T13:36:04.016706Z","iopub.execute_input":"2024-06-18T13:36:04.017181Z","iopub.status.idle":"2024-06-18T13:36:06.382761Z","shell.execute_reply.started":"2024-06-18T13:36:04.017148Z","shell.execute_reply":"2024-06-18T13:36:06.381874Z"},"trusted":true},"execution_count":77,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.96      0.76      0.85      9025\n           1       0.87      0.93      0.90      2506\n           2       0.87      0.90      0.88      2451\n           3       0.98      0.98      0.98      2092\n           4       0.91      0.97      0.93      2045\n           5       0.94      0.99      0.97      2010\n           6       0.94      0.98      0.96      1972\n           7       0.93      0.93      0.93      1278\n           8       0.76      0.90      0.83      1168\n           9       0.96      0.99      0.97      1154\n          10       0.48      0.85      0.61      1121\n          11       0.95      0.95      0.95      1017\n          12       0.88      0.97      0.92       594\n          13       0.89      0.96      0.92       521\n          14       0.87      1.00      0.93       419\n\n    accuracy                           0.89     29373\n   macro avg       0.88      0.94      0.90     29373\nweighted avg       0.91      0.89      0.89     29373\n\n","output_type":"stream"}]},{"cell_type":"code","source":"list_corr_diagnostic_2 = list_corr_diagnostic[::3]\nlist_corr_diagnostic_2.append(\"diagnostic\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from joblib import dump\ndump(model_LGBMClassifier, 'LGBMClassifier_12_led_d.joblib', compress=9)\npd.DataFrame(list_corr_diagnostic_2).to_csv(\"list_corr_diagnostic.csv\")\npd.DataFrame(list(uniques_diagnostic)).to_csv(\"uniques_diagnostic.csv\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.datasets import make_hastie_10_2\nfrom sklearn.ensemble import HistGradientBoostingClassifier\nclf = HistGradientBoostingClassifier(max_leaf_nodes=25).fit(X_train, y_train)\nclf.score(X_valid, y_valid) , accuracy_score(y_valid, clf.predict(X_valid))","metadata":{"execution":{"iopub.status.busy":"2024-06-23T11:15:52.984058Z","iopub.execute_input":"2024-06-23T11:15:52.984717Z","iopub.status.idle":"2024-06-23T11:16:07.671389Z","shell.execute_reply.started":"2024-06-23T11:15:52.984684Z","shell.execute_reply":"2024-06-23T11:16:07.670545Z"},"trusted":true},"execution_count":177,"outputs":[{"execution_count":177,"output_type":"execute_result","data":{"text/plain":"(0.8444986690328306, 0.8444986690328306)"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(y_valid, clf.predict(X_valid)))","metadata":{"execution":{"iopub.status.busy":"2024-06-23T11:16:07.674841Z","iopub.execute_input":"2024-06-23T11:16:07.676927Z","iopub.status.idle":"2024-06-23T11:16:07.904356Z","shell.execute_reply.started":"2024-06-23T11:16:07.676895Z","shell.execute_reply":"2024-06-23T11:16:07.903605Z"},"trusted":true},"execution_count":178,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.64      0.74      0.69       286\n           1       0.81      0.74      0.77       319\n           2       0.76      0.69      0.72       308\n           3       0.91      0.88      0.89       328\n           4       0.89      0.86      0.87       304\n           5       0.86      0.91      0.89       293\n           6       0.88      0.86      0.87       302\n           7       0.83      0.82      0.83       301\n           8       0.85      0.82      0.83       301\n           9       0.92      0.91      0.91       298\n          10       0.69      0.74      0.71       294\n          11       0.89      0.89      0.89       300\n          12       0.86      0.91      0.89       292\n          13       0.90      0.90      0.90       274\n          14       0.99      0.99      0.99       308\n\n    accuracy                           0.84      4508\n   macro avg       0.85      0.84      0.84      4508\nweighted avg       0.85      0.84      0.84      4508\n\n","output_type":"stream"}]},{"cell_type":"code","source":"print(classification_report(df_test['diagnostic'], clf.predict( df_test[X_train.columns])))","metadata":{"execution":{"iopub.status.busy":"2024-06-23T11:16:12.371785Z","iopub.execute_input":"2024-06-23T11:16:12.372131Z","iopub.status.idle":"2024-06-23T11:16:13.661214Z","shell.execute_reply.started":"2024-06-23T11:16:12.372105Z","shell.execute_reply":"2024-06-23T11:16:13.660146Z"},"trusted":true},"execution_count":179,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.94      0.74      0.83      9025\n           1       0.84      0.91      0.88      2506\n           2       0.84      0.84      0.84      2451\n           3       0.96      0.97      0.97      2092\n           4       0.88      0.96      0.92      2045\n           5       0.93      0.98      0.95      2010\n           6       0.93      0.95      0.94      1972\n           7       0.90      0.87      0.89      1278\n           8       0.72      0.86      0.78      1168\n           9       0.94      0.98      0.96      1154\n          10       0.43      0.79      0.56      1121\n          11       0.92      0.93      0.93      1017\n          12       0.85      0.94      0.89       594\n          13       0.83      0.91      0.87       521\n          14       0.86      1.00      0.92       419\n\n    accuracy                           0.87     29373\n   macro avg       0.85      0.91      0.87     29373\nweighted avg       0.88      0.87      0.87     29373\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from joblib import dump\ndump(clf, 'HistGradientBoostingClassifier0_12_led_d(1).joblib', compress=9)","metadata":{"execution":{"iopub.status.busy":"2024-06-21T19:06:40.005596Z","iopub.execute_input":"2024-06-21T19:06:40.006386Z","iopub.status.idle":"2024-06-21T19:06:41.591409Z","shell.execute_reply.started":"2024-06-21T19:06:40.006353Z","shell.execute_reply":"2024-06-21T19:06:41.590481Z"},"trusted":true},"execution_count":70,"outputs":[{"execution_count":70,"output_type":"execute_result","data":{"text/plain":"['HistGradientBoostingClassifier0_12_led_d(1).joblib']"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.ensemble import ExtraTreesClassifier\nfrom sklearn.model_selection import cross_val_score\nmodel_ExtraTreesClassifier = ExtraTreesClassifier(random_state=0)\nmodel_ExtraTreesClassifier.fit(X_train, y_train)\nprint(\"score \",model_ExtraTreesClassifier.score(X_train, y_train))\ny_predict = model_ExtraTreesClassifier.predict(X_valid)\nprint(\"f1_score micro \",f1_score(y_valid,y_predict, average='micro')) #\"samples\"\nprint(\"f1_score macro \",f1_score(y_valid,y_predict, average='macro'))\nprint(\"f1_score weighted \",f1_score(y_valid,y_predict, average='weighted'))\naccuracy_score(y_valid, y_predict),balanced_accuracy_score(y_valid, y_predict)","metadata":{"execution":{"iopub.status.busy":"2024-06-23T11:14:10.039994Z","iopub.execute_input":"2024-06-23T11:14:10.040540Z","iopub.status.idle":"2024-06-23T11:14:19.598620Z","shell.execute_reply.started":"2024-06-23T11:14:10.040502Z","shell.execute_reply":"2024-06-23T11:14:19.597712Z"},"trusted":true},"execution_count":174,"outputs":[{"name":"stdout","text":"score  1.0\nf1_score micro  0.8161047027506655\nf1_score macro  0.8168680253725378\nf1_score weighted  0.8156164049155675\n","output_type":"stream"},{"execution_count":174,"output_type":"execute_result","data":{"text/plain":"(0.8161047027506655, 0.8178323703707272)"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(y_valid, clf.predict(X_valid)))","metadata":{"execution":{"iopub.status.busy":"2024-06-23T11:14:19.600385Z","iopub.execute_input":"2024-06-23T11:14:19.600774Z","iopub.status.idle":"2024-06-23T11:14:19.834807Z","shell.execute_reply.started":"2024-06-23T11:14:19.600739Z","shell.execute_reply":"2024-06-23T11:14:19.834044Z"},"trusted":true},"execution_count":175,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.81      0.85      0.83       286\n           1       0.91      0.86      0.88       319\n           2       0.84      0.81      0.82       308\n           3       0.93      0.93      0.93       328\n           4       0.91      0.89      0.90       304\n           5       0.96      0.91      0.94       293\n           6       0.87      0.95      0.91       302\n           7       0.84      0.87      0.86       301\n           8       0.84      0.87      0.85       301\n           9       0.95      0.89      0.92       298\n          10       0.73      0.82      0.77       294\n          11       0.91      0.86      0.88       300\n          12       0.91      0.89      0.90       292\n          13       0.93      0.91      0.92       274\n          14       0.99      0.99      0.99       308\n\n    accuracy                           0.89      4508\n   macro avg       0.89      0.89      0.89      4508\nweighted avg       0.89      0.89      0.89      4508\n\n","output_type":"stream"}]},{"cell_type":"code","source":"print(classification_report(df_test['diagnostic'], clf.predict( df_test[X_train.columns])))","metadata":{"execution":{"iopub.status.busy":"2024-06-23T11:15:27.971210Z","iopub.execute_input":"2024-06-23T11:15:27.972145Z","iopub.status.idle":"2024-06-23T11:15:29.259382Z","shell.execute_reply.started":"2024-06-23T11:15:27.972106Z","shell.execute_reply":"2024-06-23T11:15:29.258378Z"},"trusted":true},"execution_count":176,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.96      0.71      0.82      9025\n           1       0.86      0.91      0.88      2506\n           2       0.84      0.86      0.85      2451\n           3       0.96      0.98      0.97      2092\n           4       0.88      0.96      0.92      2045\n           5       0.94      0.98      0.96      2010\n           6       0.90      0.96      0.93      1972\n           7       0.90      0.89      0.89      1278\n           8       0.71      0.88      0.78      1168\n           9       0.95      0.96      0.96      1154\n          10       0.40      0.82      0.54      1121\n          11       0.91      0.90      0.90      1017\n          12       0.85      0.94      0.90       594\n          13       0.82      0.92      0.87       521\n          14       0.85      0.99      0.91       419\n\n    accuracy                           0.86     29373\n   macro avg       0.85      0.91      0.87     29373\nweighted avg       0.89      0.86      0.86     29373\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from joblib import dump\ndump(model_ExtraTreesClassifier, 'ExtraTreesClassifier0_12_led_d(1).joblib', compress=9)","metadata":{"execution":{"iopub.status.busy":"2024-06-21T19:06:55.227900Z","iopub.execute_input":"2024-06-21T19:06:55.228628Z","iopub.status.idle":"2024-06-21T19:08:21.166842Z","shell.execute_reply.started":"2024-06-21T19:06:55.228594Z","shell.execute_reply":"2024-06-21T19:08:21.165815Z"},"trusted":true},"execution_count":72,"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"['ExtraTreesClassifier0_12_led_d(1).joblib']"},"metadata":{}}]},{"cell_type":"code","source":"diagnostic","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def X_y_func(X_diagnostic, y_diagnostic,n,count):\n    X_rhythm,  y_rhythm  = X_diagnostic[X_diagnostic.columns[38*(n):38*count]], y_diagnostic\n    X_rhythm[\"age\"]=X_diagnostic[\"age\"]\n    X_rhythm[\"sex\"]=X_diagnostic[\"sex\"]\n    X_train, X_valid, y_train, y_valid = train_test_split(X_rhythm, y_rhythm, test_size=0.1, random_state=0)\n    return X_train, X_valid, y_train, y_valid\ndef r_led(model,X_diagnostic, y_diagnostic,model_name,n,count):\n    X_train, X_valid, y_train, y_valid = X_y_func(X_diagnostic, y_diagnostic,n,count)\n    model.fit(X_train, y_train)\n    y_predict = model.predict(X_valid)\n    dump(model, model_name +str(n)+\"_\"+str(count)+'_led_d.joblib', compress=9)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nfrom warnings import simplefilter\nsimplefilter(action=\"ignore\", category=pd.errors.PerformanceWarning)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\npd.options.mode.chained_assignment = None  # default='warn'","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"r_led(LGBMClassifier(random_seed=7575),X_diagnostic, y_diagnostic,\"LGBMClassifier\",0,6)\nr_led(LGBMClassifier(random_seed=7575),X_diagnostic, y_diagnostic,\"LGBMClassifier\",6,12)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for i in range(1,13):\n    r_led(LGBMClassifier(random_seed=7575),X_diagnostic, y_diagnostic,\"LGBMClassifier\",i-1,i)\n   ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = HistGradientBoostingClassifier(max_leaf_nodes=25)\nmodel_name = \"HistGradientBoostingClassifier\"\nr_led(model,X_diagnostic, y_diagnostic,model_name,0,6)\nr_led(model,X_diagnostic, y_diagnostic,model_name,6,12)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for i in range(1,13):\n    r_led(model,X_diagnostic, y_diagnostic,model_name,i-1,i)\n   ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = ExtraTreesClassifier(random_state=0)\nmodel_name = \"ExtraTreesClassifier\"\nr_led(model,X_diagnostic, y_diagnostic,model_name,0,6)\nr_led(model,X_diagnostic, y_diagnostic,model_name,6,12)\nfor i in range(1,13):\n    r_led(model,X_diagnostic, y_diagnostic,model_name,i-1,i)\n   ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## keras","metadata":{}},{"cell_type":"code","source":"from keras import callbacks\nfrom keras.models import Sequential\nfrom keras.layers import Dense, BatchNormalization, Dropout, LSTM, Flatten\nfrom keras.layers import Conv1D, GlobalAveragePooling1D, MaxPool1D, ZeroPadding1D, LSTM, Bidirectional\nimport tensorflow as tf","metadata":{"execution":{"iopub.status.busy":"2024-06-18T13:38:04.464516Z","iopub.execute_input":"2024-06-18T13:38:04.465266Z","iopub.status.idle":"2024-06-18T13:38:14.747168Z","shell.execute_reply.started":"2024-06-18T13:38:04.465233Z","shell.execute_reply":"2024-06-18T13:38:14.746184Z"},"trusted":true},"execution_count":87,"outputs":[{"name":"stderr","text":"2024-06-18 13:38:05.914129: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-06-18 13:38:05.914232: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-06-18 13:38:06.015730: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}]},{"cell_type":"code","source":"import keras","metadata":{"execution":{"iopub.status.busy":"2024-06-18T13:38:14.748976Z","iopub.execute_input":"2024-06-18T13:38:14.750140Z","iopub.status.idle":"2024-06-18T13:38:14.755413Z","shell.execute_reply.started":"2024-06-18T13:38:14.750104Z","shell.execute_reply":"2024-06-18T13:38:14.754566Z"},"trusted":true},"execution_count":88,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.optimizers.legacy import Adam","metadata":{"execution":{"iopub.status.busy":"2024-06-18T13:38:14.756608Z","iopub.execute_input":"2024-06-18T13:38:14.756957Z","iopub.status.idle":"2024-06-18T13:38:14.787986Z","shell.execute_reply.started":"2024-06-18T13:38:14.756917Z","shell.execute_reply":"2024-06-18T13:38:14.787211Z"},"trusted":true},"execution_count":89,"outputs":[]},{"cell_type":"code","source":"df_new = ecg_df_HTV_diagnostic_n_new.dropna().copy()[numeric_cols]\nX_diagnostic, y_diagnostic   = df_new[list_corr_diagnostic[::3]], df_new['diagnostic']\n\n#X_diagnostic, y_diagnostic  = df_new[df_new.columns[:-1]], df_new['diagnostic']\ny_diagnostic =y_diagnostic.astype('int')\nsm = KMeansSMOTE(random_state=42,cluster_balance_threshold = 0.01)\nX_diagnostic, y_diagnostic = sm.fit_resample(X_diagnostic, y_diagnostic )\nsm = BorderlineSMOTE(random_state=42)\nX_diagnostic, y_diagnostic = sm.fit_resample(X_diagnostic, y_diagnostic )\nX_train, X_valid, y_train, y_valid = train_test_split(X_diagnostic, y_diagnostic , test_size=0.1, random_state=0)","metadata":{"execution":{"iopub.status.busy":"2024-06-23T11:02:57.231814Z","iopub.execute_input":"2024-06-23T11:02:57.232634Z","iopub.status.idle":"2024-06-23T11:03:46.068815Z","shell.execute_reply.started":"2024-06-23T11:02:57.232591Z","shell.execute_reply":"2024-06-23T11:03:46.067858Z"},"trusted":true},"execution_count":99,"outputs":[]},{"cell_type":"code","source":"X_train, X_valid, y_train, y_valid = train_test_split(X_diagnostic, y_diagnostic , test_size=0.1, random_state=0)","metadata":{"execution":{"iopub.status.busy":"2024-06-18T11:26:19.312488Z","iopub.execute_input":"2024-06-18T11:26:19.313294Z","iopub.status.idle":"2024-06-18T11:26:19.376448Z","shell.execute_reply.started":"2024-06-18T11:26:19.313234Z","shell.execute_reply":"2024-06-18T11:26:19.375576Z"},"trusted":true},"execution_count":57,"outputs":[]},{"cell_type":"code","source":"X_diagnostic_2, y_diagnostic_2   = df_new[list_corr_diagnostic[::3]], df_new['diagnostic']\n#X_diagnostic, y_diagnostic  = df_new[df_new.columns[:-1]], df_new['diagnostic']\ny_diagnostic_2 =y_diagnostic_2.astype('int')\nX_train, X_valid, y_train, y_valid = train_test_split(X_diagnostic_2, y_diagnostic_2 , test_size=0.1, random_state=0)","metadata":{"execution":{"iopub.status.busy":"2024-06-23T11:02:04.268061Z","iopub.execute_input":"2024-06-23T11:02:04.268782Z","iopub.status.idle":"2024-06-23T11:02:04.280804Z","shell.execute_reply.started":"2024-06-23T11:02:04.268748Z","shell.execute_reply":"2024-06-23T11:02:04.280046Z"},"trusted":true},"execution_count":92,"outputs":[]},{"cell_type":"code","source":"512/2","metadata":{"execution":{"iopub.status.busy":"2024-06-18T14:02:12.519999Z","iopub.execute_input":"2024-06-18T14:02:12.520331Z","iopub.status.idle":"2024-06-18T14:02:12.526017Z","shell.execute_reply.started":"2024-06-18T14:02:12.520306Z","shell.execute_reply":"2024-06-18T14:02:12.525037Z"},"trusted":true},"execution_count":110,"outputs":[{"execution_count":110,"output_type":"execute_result","data":{"text/plain":"256.0"},"metadata":{}}]},{"cell_type":"code","source":"early_stopping = callbacks.EarlyStopping(\n    min_delta=0.001, \n    patience=20, \n    restore_best_weights=True)\nn = 1024#84   1024\nlstm_model = Sequential()\nlstm_model.add(LSTM(n,  input_shape=(X_train.shape[1], 1), return_sequences=True))\nlstm_model.add(LSTM(n))\n#lstm_model.add(Dense(n,  input_shape=(X_train.shape[1], 1), return_sequences=True))\n#lstm_model.add(Dense(n))\nlstm_model.add(Dense(int(n/2),activation = 'relu'))\nlstm_model.add(Dropout(0.25))\nlstm_model.add(Dense(units =int(n/4), kernel_initializer = 'uniform', activation = 'relu'))\nlstm_model.add(Dropout(0.25))\n#lstm_model.add(Dense(int(n/4), bias_regularizer=tf.keras.regularizers.l2(0.01)))\n#lstm_model.add(Dropout(0.25))\n\nlstm_model.add(Dense(len(y_train.value_counts().index), activation = 'sigmoid'))\nlstm_model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\nhistory = lstm_model.fit(X_train, y_train, batch_size = 126, epochs = 15, verbose=1, validation_split=0.2)\n","metadata":{"execution":{"iopub.status.busy":"2024-06-18T15:45:53.914280Z","iopub.execute_input":"2024-06-18T15:45:53.914649Z","iopub.status.idle":"2024-06-18T16:15:12.849366Z","shell.execute_reply.started":"2024-06-18T15:45:53.914618Z","shell.execute_reply":"2024-06-18T16:15:12.848543Z"},"trusted":true},"execution_count":154,"outputs":[{"name":"stdout","text":"Epoch 1/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 143ms/step - accuracy: 0.3143 - loss: 2.0783 - val_accuracy: 0.5774 - val_loss: 1.2734\nEpoch 2/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m110s\u001b[0m 142ms/step - accuracy: 0.6546 - loss: 1.0642 - val_accuracy: 0.6924 - val_loss: 1.0135\nEpoch 3/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 143ms/step - accuracy: 0.7516 - loss: 0.7812 - val_accuracy: 0.7964 - val_loss: 0.6412\nEpoch 4/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 143ms/step - accuracy: 0.8279 - loss: 0.5413 - val_accuracy: 0.8545 - val_loss: 0.4741\nEpoch 5/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 143ms/step - accuracy: 0.8787 - loss: 0.3890 - val_accuracy: 0.8702 - val_loss: 0.4413\nEpoch 6/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 143ms/step - accuracy: 0.9037 - loss: 0.3044 - val_accuracy: 0.8922 - val_loss: 0.3764\nEpoch 7/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 143ms/step - accuracy: 0.9237 - loss: 0.2406 - val_accuracy: 0.8959 - val_loss: 0.3716\nEpoch 8/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 143ms/step - accuracy: 0.9387 - loss: 0.1923 - val_accuracy: 0.8944 - val_loss: 0.3728\nEpoch 9/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 143ms/step - accuracy: 0.9502 - loss: 0.1572 - val_accuracy: 0.9161 - val_loss: 0.3329\nEpoch 10/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 143ms/step - accuracy: 0.9582 - loss: 0.1317 - val_accuracy: 0.9122 - val_loss: 0.3772\nEpoch 11/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 143ms/step - accuracy: 0.9628 - loss: 0.1176 - val_accuracy: 0.9147 - val_loss: 0.3784\nEpoch 12/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 143ms/step - accuracy: 0.9653 - loss: 0.1059 - val_accuracy: 0.9144 - val_loss: 0.3858\nEpoch 13/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 143ms/step - accuracy: 0.9697 - loss: 0.0952 - val_accuracy: 0.9190 - val_loss: 0.3772\nEpoch 14/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 144ms/step - accuracy: 0.9753 - loss: 0.0781 - val_accuracy: 0.9189 - val_loss: 0.4110\nEpoch 15/15\n\u001b[1m775/775\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 143ms/step - accuracy: 0.9740 - loss: 0.0818 - val_accuracy: 0.9223 - val_loss: 0.3826\n","output_type":"stream"}]},{"cell_type":"code","source":"val_accuracy = np.mean(history.history['val_accuracy'])\nprint(\"\\n%s: %.2f%%\" % ('val_accuracy', np.mean(history.history['val_accuracy'])*100))","metadata":{"execution":{"iopub.status.busy":"2024-06-18T16:15:12.851864Z","iopub.execute_input":"2024-06-18T16:15:12.852146Z","iopub.status.idle":"2024-06-18T16:15:12.858015Z","shell.execute_reply.started":"2024-06-18T16:15:12.852122Z","shell.execute_reply":"2024-06-18T16:15:12.857158Z"},"trusted":true},"execution_count":155,"outputs":[{"name":"stdout","text":"\nval_accuracy: 85.94%\n","output_type":"stream"}]},{"cell_type":"code","source":"keras.utils.plot_model(lstm_model, 'multi_input_and_output_model.png', show_shapes=True)","metadata":{"execution":{"iopub.status.busy":"2024-06-18T16:18:19.702920Z","iopub.execute_input":"2024-06-18T16:18:19.703314Z","iopub.status.idle":"2024-06-18T16:18:20.075458Z","shell.execute_reply.started":"2024-06-18T16:18:19.703283Z","shell.execute_reply":"2024-06-18T16:18:20.074363Z"},"trusted":true},"execution_count":161,"outputs":[{"execution_count":161,"output_type":"execute_result","data":{"image/png":"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","text/plain":"<IPython.core.display.Image object>"},"metadata":{}}]},{"cell_type":"code","source":"val_accuracy = np.mean(history.history['accuracy'])\nprint(\"\\n%s: %.2f%%\" % ('accuracy', np.mean(history.history['accuracy'])*100))","metadata":{"execution":{"iopub.status.busy":"2024-06-18T16:15:15.532581Z","iopub.execute_input":"2024-06-18T16:15:15.533255Z","iopub.status.idle":"2024-06-18T16:15:15.538394Z","shell.execute_reply.started":"2024-06-18T16:15:15.533221Z","shell.execute_reply":"2024-06-18T16:15:15.537450Z"},"trusted":true},"execution_count":156,"outputs":[{"name":"stdout","text":"\naccuracy: 87.61%\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.legend((\"accuracy\",\"val_accuracy\"))\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')","metadata":{"execution":{"iopub.status.busy":"2024-06-18T16:15:17.940620Z","iopub.execute_input":"2024-06-18T16:15:17.941260Z","iopub.status.idle":"2024-06-18T16:15:18.173962Z","shell.execute_reply.started":"2024-06-18T16:15:17.941228Z","shell.execute_reply":"2024-06-18T16:15:18.173120Z"},"trusted":true},"execution_count":157,"outputs":[{"execution_count":157,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Accuracy')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"m =pd.DataFrame(lstm_model.predict(X_valid)).T.idxmax()","metadata":{"execution":{"iopub.status.busy":"2024-06-18T16:15:21.780281Z","iopub.execute_input":"2024-06-18T16:15:21.780656Z","iopub.status.idle":"2024-06-18T16:15:28.829319Z","shell.execute_reply.started":"2024-06-18T16:15:21.780626Z","shell.execute_reply":"2024-06-18T16:15:28.828387Z"},"trusted":true},"execution_count":158,"outputs":[{"name":"stdout","text":"\u001b[1m424/424\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 16ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(y_valid, m))","metadata":{"execution":{"iopub.status.busy":"2024-06-18T16:15:28.830907Z","iopub.execute_input":"2024-06-18T16:15:28.831187Z","iopub.status.idle":"2024-06-18T16:15:28.853185Z","shell.execute_reply.started":"2024-06-18T16:15:28.831163Z","shell.execute_reply":"2024-06-18T16:15:28.852303Z"},"trusted":true},"execution_count":159,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.78      0.72      0.75       933\n           1       0.89      0.86      0.88       873\n           2       0.86      0.89      0.87       876\n           3       0.95      0.93      0.94       900\n           4       0.89      0.88      0.89       819\n           5       0.92      0.93      0.93       922\n           6       0.94      0.92      0.93       912\n           7       0.96      0.95      0.95       912\n           8       0.92      0.93      0.93       902\n           9       0.93      0.96      0.94       960\n          10       0.89      0.93      0.91       888\n          11       0.94      0.95      0.95       882\n          12       0.96      0.98      0.97       908\n          13       0.98      0.97      0.98       906\n          14       0.99      0.99      0.99       954\n\n    accuracy                           0.92     13547\n   macro avg       0.92      0.92      0.92     13547\nweighted avg       0.92      0.92      0.92     13547\n\n","output_type":"stream"}]},{"cell_type":"code","source":"m =pd.DataFrame(lstm_model.predict(df_test[X_train.columns])).T.idxmax()\nprint(classification_report( df_test['diagnostic'], m))","metadata":{"execution":{"iopub.status.busy":"2024-06-18T16:15:28.854352Z","iopub.execute_input":"2024-06-18T16:15:28.854793Z","iopub.status.idle":"2024-06-18T16:15:43.670725Z","shell.execute_reply.started":"2024-06-18T16:15:28.854759Z","shell.execute_reply":"2024-06-18T16:15:43.669778Z"},"trusted":true},"execution_count":160,"outputs":[{"name":"stdout","text":"\u001b[1m918/918\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 16ms/step\n              precision    recall  f1-score   support\n\n           0       0.94      0.90      0.92      9025\n           1       0.88      0.85      0.86      2506\n           2       0.87      0.87      0.87      2451\n           3       0.94      0.92      0.93      2092\n           4       0.88      0.88      0.88      2045\n           5       0.88      0.92      0.90      2010\n           6       0.90      0.90      0.90      1972\n           7       0.88      0.93      0.90      1278\n           8       0.83      0.85      0.84      1168\n           9       0.85      0.93      0.89      1154\n          10       0.79      0.88      0.83      1121\n          11       0.83      0.90      0.86      1017\n          12       0.85      0.94      0.89       594\n          13       0.91      0.82      0.86       521\n          14       0.88      0.96      0.92       419\n\n    accuracy                           0.89     29373\n   macro avg       0.87      0.90      0.88     29373\nweighted avg       0.90      0.89      0.89     29373\n\n","output_type":"stream"}]},{"cell_type":"code","source":"import keras\nmodel.save('lstm_model_d.keras')","metadata":{"execution":{"iopub.status.busy":"2024-06-13T08:05:57.472726Z","iopub.execute_input":"2024-06-13T08:05:57.473113Z","iopub.status.idle":"2024-06-13T08:05:57.976358Z","shell.execute_reply.started":"2024-06-13T08:05:57.473082Z","shell.execute_reply":"2024-06-13T08:05:57.975569Z"},"trusted":true},"execution_count":63,"outputs":[]},{"cell_type":"code","source":"lstm_model.save('lstm_model_d.h5')\nlstm_model.save('lstm_model_d.keras')","metadata":{},"execution_count":null,"outputs":[]}]}